yanghaoran
Gitee
3 years ago
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GPG Key ID: 173E9B9CA92EEF8F
46 changed files with 592 additions and 460 deletions
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+2 -1inc/external/acl/acl_op_compiler.h
-
+2 -2inc/external/acl/acl_prof.h
-
+1 -0inc/external/acl/error_codes/rt_error_codes.h
-
+14 -4inc/external/ge/ge_api_types.h
-
+1 -0inc/external/runtime/rt_error_codes.h
-
+0 -22inc/framework/common/aicpu_op.h
-
+0 -51inc/framework/common/file_constant_util.h
-
+1 -4inc/framework/common/fmk_error_codes.h
-
+0 -85inc/framework/common/gflags_util.h
-
+2 -2inc/framework/common/helper/model_helper.h
-
+0 -43inc/framework/common/op/attr_value_util.h
-
+1 -14inc/framework/common/string_util.h
-
+2 -0inc/framework/common/types.h
-
+1 -1inc/framework/executor/ge_executor.h
-
+1 -4inc/framework/memory/memory_api.h
-
+0 -35inc/framework/omg/model_tool.h
-
+1 -0inc/framework/omg/omg_inner_types.h
-
+3 -2inc/framework/runtime/model_v2_executor.h
-
+1 -2inc/framework/runtime/subscriber/built_in_subscriber_definitions.h
-
+1 -4inc/framework/runtime/subscriber/executor_subscribers_scheduler.h
-
+5 -0inc/framework/runtime/subscriber/global_profiling.h
-
+1 -1metadef
-
+4 -4third_party/fwkacllib/inc/cce/aicpu_engine_struct.h
-
+5 -5third_party/fwkacllib/inc/cce/fwk_adpt_struct.h
-
+1 -0third_party/fwkacllib/inc/external/runtime/rt_error_codes.h
-
+1 -6third_party/fwkacllib/inc/ops/OWNERS
-
+1 -1third_party/fwkacllib/inc/ops/cluster.h
-
+2 -2third_party/fwkacllib/inc/ops/elewise_calculation_ops.h
-
+140 -0third_party/fwkacllib/inc/ops/experiment_ops.h
-
+14 -14third_party/fwkacllib/inc/ops/image_ops.h
-
+1 -1third_party/fwkacllib/inc/ops/map_ops.h
-
+4 -4third_party/fwkacllib/inc/ops/math_ops.h
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+31 -39third_party/fwkacllib/inc/ops/matrix_calculation_ops.h
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+2 -2third_party/fwkacllib/inc/ops/nn_batch_norm_ops.h
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+54 -65third_party/fwkacllib/inc/ops/nn_calculation_ops.h
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+107 -0third_party/fwkacllib/inc/ops/nn_norm_ops.h
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+23 -23third_party/fwkacllib/inc/ops/nn_pooling_ops.h
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+3 -3third_party/fwkacllib/inc/ops/reduce_ops.h
-
+2 -1third_party/fwkacllib/inc/runtime/config.h
-
+4 -4third_party/fwkacllib/inc/runtime/dev.h
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+34 -1third_party/fwkacllib/inc/runtime/mem.h
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+37 -4third_party/fwkacllib/inc/runtime/rt_mem_queue.h
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+60 -2third_party/fwkacllib/inc/runtime/rt_stars.h
-
+11 -1third_party/fwkacllib/inc/runtime/stream.h
-
+5 -1third_party/fwkacllib/inc/toolchain/prof_acl_api.h
-
+6 -0third_party/fwkacllib/inc/toolchain/prof_common.h
+ 2
- 1
inc/external/acl/acl_op_compiler.h
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| @@ -36,7 +36,8 @@ typedef enum { | |||
| ACL_DEBUG_DIR, | |||
| ACL_OP_COMPILER_CACHE_MODE, | |||
| ACL_OP_COMPILER_CACHE_DIR, | |||
| ACL_OP_PERFORMANCE_MODE | |||
| ACL_OP_PERFORMANCE_MODE, | |||
| ACL_OP_JIT_COMPILE | |||
| } aclCompileOpt; | |||
| typedef enum aclCompileFlag { ACL_OP_COMPILE_DEFAULT, ACL_OP_COMPILE_FUZZ } aclOpCompileFlag; | |||
+ 2
- 2
inc/external/acl/acl_prof.h
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| @@ -128,8 +128,8 @@ MSVP_PROF_API aclError aclprofStart(const aclprofConfig *profilerConfig); | |||
| * @see aclprofDestroyConfig | |||
| */ | |||
| MSVP_PROF_API aclprofConfig *aclprofCreateConfig(uint32_t *deviceIdList, uint32_t deviceNums, | |||
| aclprofAicoreMetrics aicoreMetrics, aclprofAicoreEvents *aicoreEvents, | |||
| uint64_t dataTypeConfig); | |||
| aclprofAicoreMetrics aicoreMetrics, | |||
| const aclprofAicoreEvents *aicoreEvents, uint64_t dataTypeConfig); | |||
| /** | |||
| * @ingroup AscendCL | |||
+ 1
- 0
inc/external/acl/error_codes/rt_error_codes.h
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| @@ -113,6 +113,7 @@ static const int32_t ACL_ERROR_RT_AICORE_TRAP_READ_OVERFLOW = 507042; // | |||
| static const int32_t ACL_ERROR_RT_AICORE_TRAP_WRITE_OVERFLOW = 507043; // aic trap write overflow | |||
| static const int32_t ACL_ERROR_RT_VECTOR_CORE_TRAP_READ_OVERFLOW = 507044; // aiv trap read overflow | |||
| static const int32_t ACL_ERROR_RT_VECTOR_CORE_TRAP_WRITE_OVERFLOW = 507045; // aiv trap write overflow | |||
| static const int32_t ACL_ERROR_RT_STREAM_SYNC_TIMEOUT = 507046; // stream sync time out | |||
| static const int32_t ACL_ERROR_RT_DRV_INTERNAL_ERROR = 507899; // drv internal error | |||
| static const int32_t ACL_ERROR_RT_AICPU_INTERNAL_ERROR = 507900; // aicpu internal error | |||
+ 14
- 4
inc/external/ge/ge_api_types.h
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| @@ -30,6 +30,8 @@ namespace ge { | |||
| // Option key: graph run mode | |||
| const char_t *const OPTION_GRAPH_RUN_MODE = "ge.graphRunMode"; | |||
| const char_t *const OPTION_DEVICE_TYPE = "ge.deviceType"; | |||
| // Option key: topo sorting mode | |||
| const char *const OPTION_TOPO_SORTING_MODE = "ge.topoSortingMode"; | |||
| // Option key: ome init | |||
| const char_t *const OPTION_EXEC_SESSION_ID = "ge.exec.sessionId"; | |||
| @@ -129,6 +131,7 @@ const char_t *const MODIFY_MIXLIST = "ge.exec.modify_mixlist"; | |||
| const char_t *const OP_PRECISION_MODE = "ge.exec.op_precision_mode"; | |||
| const char_t *const CUSTOMIZE_DTYPES = "ge.customizeDtypes"; | |||
| const char_t *const COMPRESSION_OPTIMIZE_CONF = "ge.compressionOptimizeConf"; | |||
| const char_t *const BUILD_GRAPH_ALREADY_INITIALIZED = "build_graph_already_initialized"; | |||
| } // namespace configure_option | |||
| // Configure stream num by Session constructor options param, | |||
| // its value should be int32_t type, default value is "1" | |||
| @@ -293,6 +296,9 @@ const std::string FUSION_SWITCH_FILE = "ge.fusionSwitchFile"; | |||
| // Configure compression optimize file path | |||
| const std::string COMPRESSION_OPTIMIZE_CONF = "ge.compressionOptimizeConf"; | |||
| // Configure for amct | |||
| const std::string BUILD_GRAPH_ALREADY_INITIALIZED = "build_graph_already_initialized"; | |||
| // Configure customize dtypes path | |||
| const std::string CUSTOMIZE_DTYPES = "ge.customizeDtypes"; | |||
| @@ -394,7 +400,8 @@ const char_t *const GRAPH_PARALLEL_OPTION_PATH = "ge.graphParallelOptionPath"; | |||
| const char_t *const EVALUATE_GRAPH_RESOURCE_MODE = "ge.evaluateGraphResourceMode"; | |||
| // Graph run mode | |||
| enum GraphRunMode { PREDICTION = 0, TRAIN }; | |||
| // Topo sorting mode | |||
| enum class TopoSortingMode { BFS = 0, DFS = 1 }; | |||
| // Input/Output tensor info | |||
| struct InputTensorInfo { | |||
| uint32_t data_type; // data type | |||
| @@ -478,6 +485,8 @@ static const char_t *const MODIFY_MIXLIST = ge::MODIFY_MIXLIST.c_str(); | |||
| static const char_t *const OP_PRECISION_MODE = ge::OP_PRECISION_MODE.c_str(); | |||
| static const char_t *const CUSTOMIZE_DTYPES = "ge.customizeDtypes"; | |||
| static const char_t *const COMPRESSION_OPTIMIZE_CONF = "ge.compressionOptimizeConf"; | |||
| static const char_t *const BUILD_GRAPH_ALREADY_INITIALIZED = "build_graph_already_initialized"; | |||
| static const char_t *const INPUT_DATA_NAMES = "input_data_names"; | |||
| // for interface: aclgrphBuildModel | |||
| #ifdef __GNUC__ | |||
| @@ -514,8 +523,8 @@ const std::set<std::string> ir_builder_suppported_options = {INPUT_FORMAT, | |||
| // for interface: aclgrphParse | |||
| const std::set<std::string> ir_parser_suppported_options = { | |||
| INPUT_FP16_NODES, IS_INPUT_ADJUST_HW_LAYOUT, IS_OUTPUT_ADJUST_HW_LAYOUT, OUTPUT, | |||
| OUT_NODES, ENABLE_SCOPE_FUSION_PASSES}; | |||
| INPUT_FP16_NODES, IS_INPUT_ADJUST_HW_LAYOUT, IS_OUTPUT_ADJUST_HW_LAYOUT, OUTPUT, OUT_NODES, | |||
| ENABLE_SCOPE_FUSION_PASSES, INPUT_DATA_NAMES}; | |||
| // for interface: aclgrphBuildInitialize | |||
| const std::set<std::string> global_options = {CORE_TYPE, | |||
| @@ -540,7 +549,8 @@ const std::set<std::string> global_options = {CORE_TYPE, | |||
| OP_COMPILER_CACHE_DIR, | |||
| OP_COMPILER_CACHE_MODE, | |||
| MODIFY_MIXLIST, | |||
| COMPRESSION_OPTIMIZE_CONF}; | |||
| COMPRESSION_OPTIMIZE_CONF, | |||
| BUILD_GRAPH_ALREADY_INITIALIZED}; | |||
| #endif | |||
| } // namespace ir_option | |||
| } // namespace ge | |||
+ 1
- 0
inc/external/runtime/rt_error_codes.h
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| @@ -113,6 +113,7 @@ static const int32_t ACL_ERROR_RT_AICORE_TRAP_READ_OVERFLOW = 507042; // | |||
| static const int32_t ACL_ERROR_RT_AICORE_TRAP_WRITE_OVERFLOW = 507043; // aic trap write overflow | |||
| static const int32_t ACL_ERROR_RT_VECTOR_CORE_TRAP_READ_OVERFLOW = 507044; // aiv trap read overflow | |||
| static const int32_t ACL_ERROR_RT_VECTOR_CORE_TRAP_WRITE_OVERFLOW = 507045; // aiv trap write overflow | |||
| static const int32_t ACL_ERROR_RT_STREAM_SYNC_TIMEOUT = 507046; // stream sync time out | |||
| static const int32_t ACL_ERROR_RT_DRV_INTERNAL_ERROR = 507899; // drv internal error | |||
| static const int32_t ACL_ERROR_RT_AICPU_INTERNAL_ERROR = 507900; // aicpu internal error | |||
+ 0
- 22
inc/framework/common/aicpu_op.h
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| @@ -1,22 +0,0 @@ | |||
| /** | |||
| * Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved. | |||
| * | |||
| * Licensed under the Apache License, Version 2.0 (the "License"); | |||
| * you may not use this file except in compliance with the License. | |||
| * You may obtain a copy of the License at | |||
| * | |||
| * http://www.apache.org/licenses/LICENSE-2.0 | |||
| * | |||
| * Unless required by applicable law or agreed to in writing, software | |||
| * distributed under the License is distributed on an "AS IS" BASIS, | |||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
| * See the License for the specific language governing permissions and | |||
| * limitations under the License. | |||
| */ | |||
| #ifndef INC_FRAMEWORK_COMMON_AICPU_OP_H_ | |||
| #define INC_FRAMEWORK_COMMON_AICPU_OP_H_ | |||
| #include "cce/customize.h" | |||
| #endif // INC_FRAMEWORK_COMMON_AICPU_OP_H_ | |||
+ 0
- 51
inc/framework/common/file_constant_util.h
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| @@ -1,51 +0,0 @@ | |||
| /** | |||
| * Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved. | |||
| * | |||
| * Licensed under the Apache License, Version 2.0 (the "License"); | |||
| * you may not use this file except in compliance with the License. | |||
| * You may obtain a copy of the License at | |||
| * | |||
| * http://www.apache.org/licenses/LICENSE-2.0 | |||
| * | |||
| * Unless required by applicable law or agreed to in writing, software | |||
| * distributed under the License is distributed on an "AS IS" BASIS, | |||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
| * See the License for the specific language governing permissions and | |||
| * limitations under the License. | |||
| */ | |||
| #ifndef INC_FRAMEWORK_COMMON_FILE_CONSTANT_UTIL_H | |||
| #define INC_FRAMEWORK_COMMON_FILE_CONSTANT_UTIL_H | |||
| #include <map> | |||
| #include <string> | |||
| #include <vector> | |||
| #include "ge/ge_api_error_codes.h" | |||
| #include "nlohmann/json.hpp" | |||
| #include "graph/op_desc.h" | |||
| #include "graph/ge_tensor.h" | |||
| namespace ge { | |||
| struct FileConstantInfo { | |||
| std::string value_bin_file_id; | |||
| std::string value_bin_file_path; | |||
| }; | |||
| struct OptionInfo { | |||
| std::vector<FileConstantInfo> info; | |||
| }; | |||
| void from_json(const nlohmann::json &j, FileConstantInfo &info); | |||
| void from_json(const nlohmann::json &j, OptionInfo &option_info); | |||
| Status GetFilePathFromOption(std::map<std::string, std::string> &file_id_and_path_map); | |||
| Status CopyOneWeightFromFile(const void *const curr_dev_ptr, const std::string &value, const size_t file_constant_size, | |||
| size_t &left_size); | |||
| Status GetFilePath(const OpDescPtr &op_desc, const std::map<std::string, std::string> &file_id_and_path_map, | |||
| std::string &file_path); | |||
| } // namespace ge | |||
| #endif // INC_FRAMEWORK_COMMON_FILE_CONSTANT_UTIL_H | |||
+ 1
- 4
inc/framework/common/fmk_error_codes.h
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| @@ -19,18 +19,14 @@ | |||
| #if defined(_MSC_VER) | |||
| #ifdef FUNC_VISIBILITY | |||
| #define GE_FUNC_VISIBILITY _declspec(dllexport) | |||
| #define GE_OBJECT_VISIBILITY | |||
| #else | |||
| #define GE_FUNC_VISIBILITY | |||
| #define GE_OBJECT_VISIBILITY | |||
| #endif | |||
| #else | |||
| #ifdef FUNC_VISIBILITY | |||
| #define GE_FUNC_VISIBILITY __attribute__((visibility("default"))) | |||
| #define GE_OBJECT_VISIBILITY | |||
| #else | |||
| #define GE_FUNC_VISIBILITY | |||
| #define GE_OBJECT_VISIBILITY __attribute__((visibility("hidden"))) | |||
| #endif | |||
| #endif | |||
| @@ -40,6 +36,7 @@ | |||
| #include "framework/common/fmk_types.h" | |||
| #include "register/register_error_codes.h" | |||
| #include "external/ge/ge_error_codes.h" | |||
| // Each module uses the following four macros to define error codes: | |||
| #define DECLARE_ERRORNO_OMG(name, value) DECLARE_ERRORNO(SYSID_FWK, MODID_OMG, name, value) | |||
+ 0
- 85
inc/framework/common/gflags_util.h
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| @@ -1,85 +0,0 @@ | |||
| /** | |||
| * Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved. | |||
| * | |||
| * Licensed under the Apache License, Version 2.0 (the "License"); | |||
| * you may not use this file except in compliance with the License. | |||
| * You may obtain a copy of the License at | |||
| * | |||
| * http://www.apache.org/licenses/LICENSE-2.0 | |||
| * | |||
| * Unless required by applicable law or agreed to in writing, software | |||
| * distributed under the License is distributed on an "AS IS" BASIS, | |||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
| * See the License for the specific language governing permissions and | |||
| * limitations under the License. | |||
| */ | |||
| #ifndef INC_FRAMEWORK_COMMON_GFLAGS_UTIL_H_ | |||
| #define INC_FRAMEWORK_COMMON_GFLAGS_UTIL_H_ | |||
| #if defined(_MSC_VER) | |||
| #ifdef FUNC_VISIBILITY | |||
| #define GE_FUNC_VISIBILITY _declspec(dllexport) | |||
| #else | |||
| #define GE_FUNC_VISIBILITY | |||
| #endif | |||
| #else | |||
| #ifdef FUNC_VISIBILITY | |||
| #define GE_FUNC_VISIBILITY __attribute__((visibility("default"))) | |||
| #else | |||
| #define GE_FUNC_VISIBILITY | |||
| #endif | |||
| #endif | |||
| #include <gflags/gflags.h> | |||
| #include <string> | |||
| namespace ge { | |||
| class GE_FUNC_VISIBILITY GflagsUtils { | |||
| public: | |||
| static bool IsSetCommandTrue(const char *name) { | |||
| std::string out; | |||
| return gflags::GetCommandLineOption(name, &out) && out == "true"; | |||
| } | |||
| /// | |||
| /// @brief Determines whether the parameter is empty | |||
| /// @param name name parameter name | |||
| /// @return true if empty otherwise false | |||
| /// | |||
| static bool IsSetCommandNotEmpty(const char *name) { | |||
| std::string out; | |||
| return gflags::GetCommandLineOption(name, &out) && !out.empty(); | |||
| } | |||
| /// | |||
| /// @brief Determines whether the parameter is not default | |||
| /// @param flag_name name parameter name | |||
| /// @return true if not default otherwise false | |||
| /// | |||
| static bool IsCommandLineNotDefault(const char *flag_name) { | |||
| google::CommandLineFlagInfo info; | |||
| return GetCommandLineFlagInfo(flag_name, &info) && !info.is_default; | |||
| } | |||
| /// | |||
| /// @brief Modify gflags to print help information | |||
| /// @param flags_h Pass in the self-defined help parameter, it is recommended to be FLAGS_h | |||
| /// @return void | |||
| /// | |||
| static void ChangeHelpFlags(bool flags_h) { | |||
| if (flags_h || IsSetCommandTrue("help") || IsSetCommandTrue("helpfull") || IsSetCommandNotEmpty("helpon") || | |||
| IsSetCommandNotEmpty("helpmatch") || IsSetCommandTrue("helppackage") || IsSetCommandTrue("helpxml")) { | |||
| gflags::SetCommandLineOption("help", "false"); | |||
| gflags::SetCommandLineOption("helpfull", "false"); | |||
| gflags::SetCommandLineOption("helpon", ""); | |||
| gflags::SetCommandLineOption("helpmatch", ""); | |||
| gflags::SetCommandLineOption("helppackage", "false"); | |||
| gflags::SetCommandLineOption("helpxml", "false"); | |||
| gflags::SetCommandLineOption("helpshort", "true"); | |||
| } | |||
| } | |||
| }; | |||
| } // namespace ge | |||
| #endif // INC_FRAMEWORK_COMMON_GFLAGS_UTIL_H_ | |||
+ 2
- 2
inc/framework/common/helper/model_helper.h
View File
| @@ -34,6 +34,8 @@ class GE_FUNC_VISIBILITY ModelHelper { | |||
| Status SaveToOmModel(const GeModelPtr &ge_model, const SaveParam &save_param, const std::string &output_file, | |||
| ge::ModelBufferData &model) const; | |||
| Status GenerateGeModel(const OmFileLoadHelper &om_load_helper, GeModelPtr &cur_model, const size_t mode_index, | |||
| const bool is_dyn_root) const; | |||
| Status SaveToOmRootModel(const GeRootModelPtr &ge_root_model, const SaveParam &save_param, | |||
| const std::string &output_file, ModelBufferData &model, const bool is_unknown_shape) const; | |||
| Status SaveOriginalGraphToOmModel(const ge::Graph &graph, const std::string &output_file) const; | |||
| @@ -67,8 +69,6 @@ class GE_FUNC_VISIBILITY ModelHelper { | |||
| bool IsPartitionedGraph(const GeModelPtr &cur_model) const; | |||
| Status GenerateGeModel(const OmFileLoadHelper &om_load_helper, GeModelPtr &cur_model, const size_t mode_index, | |||
| const bool is_dyn_root) const; | |||
| Status GenerateGeRootModel(const OmFileLoadHelper &om_load_helper); | |||
| Status LoadModelData(const OmFileLoadHelper &om_load_helper, const GeModelPtr &cur_model, | |||
+ 0
- 43
inc/framework/common/op/attr_value_util.h
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| @@ -1,43 +0,0 @@ | |||
| /** | |||
| * Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved. | |||
| * | |||
| * Licensed under the Apache License, Version 2.0 (the "License"); | |||
| * you may not use this file except in compliance with the License. | |||
| * You may obtain a copy of the License at | |||
| * | |||
| * http://www.apache.org/licenses/LICENSE-2.0 | |||
| * | |||
| * Unless required by applicable law or agreed to in writing, software | |||
| * distributed under the License is distributed on an "AS IS" BASIS, | |||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
| * See the License for the specific language governing permissions and | |||
| * limitations under the License. | |||
| */ | |||
| #ifndef INC_FRAMEWORK_COMMON_OP_ATTR_VALUE_UTIL_H_ | |||
| #define INC_FRAMEWORK_COMMON_OP_ATTR_VALUE_UTIL_H_ | |||
| #if defined(_MSC_VER) | |||
| #ifdef FUNC_VISIBILITY | |||
| #define GE_FUNC_VISIBILITY _declspec(dllexport) | |||
| #else | |||
| #define GE_FUNC_VISIBILITY | |||
| #endif | |||
| #else | |||
| #ifdef FUNC_VISIBILITY | |||
| #define GE_FUNC_VISIBILITY __attribute__((visibility("default"))) | |||
| #else | |||
| #define GE_FUNC_VISIBILITY | |||
| #endif | |||
| #endif | |||
| #include <google/protobuf/map.h> | |||
| #include <unordered_map> | |||
| #include <string> | |||
| #include "graph/debug/ge_attr_define.h" | |||
| #include "proto/om.pb.h" | |||
| namespace ge { | |||
| GE_FUNC_VISIBILITY void SetAttrDef(const std::string &value, domi::AttrDef *const out); | |||
| } | |||
| #endif // INC_FRAMEWORK_COMMON_OP_ATTR_VALUE_UTIL_H_ | |||
+ 1
- 14
inc/framework/common/string_util.h
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| @@ -17,20 +17,6 @@ | |||
| #ifndef INC_FRAMEWORK_COMMON_STRING_UTIL_H_ | |||
| #define INC_FRAMEWORK_COMMON_STRING_UTIL_H_ | |||
| #if defined(_MSC_VER) | |||
| #ifdef FUNC_VISIBILITY | |||
| #define GE_FUNC_VISIBILITY _declspec(dllexport) | |||
| #else | |||
| #define GE_FUNC_VISIBILITY | |||
| #endif | |||
| #else | |||
| #ifdef FUNC_VISIBILITY | |||
| #define GE_FUNC_VISIBILITY __attribute__((visibility("default"))) | |||
| #else | |||
| #define GE_FUNC_VISIBILITY | |||
| #endif | |||
| #endif | |||
| #include <cctype> | |||
| #include <securec.h> | |||
| @@ -40,6 +26,7 @@ | |||
| #include <string> | |||
| #include <vector> | |||
| #include "graph/types.h" | |||
| #include "external/ge/ge_error_codes.h" | |||
| namespace ge { | |||
| class GE_FUNC_VISIBILITY StringUtils { | |||
+ 2
- 0
inc/framework/common/types.h
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| @@ -45,7 +45,9 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const std::string PROFIL | |||
| FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const std::string MODEL_ATTR_TASKS; | |||
| FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const std::string MODEL_ATTR_TASK_GEN_BASE_ADDR; | |||
| FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const std::string MODEL_ATTR_TASK_GEN_HOST_BASE_ADDR; | |||
| FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const std::string MODEL_ATTR_TASK_GEN_HOST_SVM_BASE_ADDR; | |||
| FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const std::string MODEL_ATTR_HOST_MEMORY_SIZE; | |||
| FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const std::string MODEL_ATTR_HOST_SVM_SIZE; | |||
| FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const std::string MODEL_ATTR_TASK_GEN_WEIGHT_ADDR; | |||
| FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const std::string MODEL_ATTR_FUSION_MODEL_DEF; | |||
| FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const uint64_t ALLOC_MEMORY_MAX_SIZE; // Max size of 8 GB. | |||
+ 1
- 1
inc/framework/executor/ge_executor.h
View File
| @@ -323,7 +323,7 @@ class GE_FUNC_VISIBILITY GeExecutor { | |||
| static Status ReleaseSingleOpResource(void *const stream); | |||
| static Status ClearCustomAicpuSo(); | |||
| static Status ClearCustomAicpuSo(const uint32_t &device_id); | |||
| static Status GetDeviceIdByModelId(const uint32_t model_id, uint32_t &device_id); | |||
+ 1
- 4
inc/framework/memory/memory_api.h
View File
| @@ -25,6 +25,7 @@ enum MemStorageType { | |||
| HBM = 0, | |||
| RDMA_HBM, | |||
| HOST_DDR, | |||
| HOST_SVM, | |||
| }; | |||
| struct HostVarInfo { | |||
| @@ -38,27 +39,23 @@ struct TensorInfo { | |||
| DataType data_type; | |||
| }; | |||
| /// | |||
| /// \param size [in] rdma pool memory size to be allocated. | |||
| /// \param mem_type [in] memory type for rdma pool. | |||
| /// \return Status result of function | |||
| GE_FUNC_VISIBILITY Status InitRdmaPool(size_t size, rtMemType_t mem_type = RT_MEMORY_HBM); | |||
| /// | |||
| /// \param var_info [in] host variable addr infos. | |||
| /// \param mem_type [in] memory type for rdma pool. | |||
| /// \return Status result of function | |||
| GE_FUNC_VISIBILITY Status RdmaRemoteRegister(const std::vector<HostVarInfo> &var_info, | |||
| rtMemType_t mem_type = RT_MEMORY_HBM); | |||
| /// | |||
| /// \param tensor_info [in] description for tensor stored shared memory. | |||
| /// \param dev_addr [out] malloced shared memory addr. | |||
| /// \param memory_size [out] malloced shared memory size. | |||
| /// \return Status result of function | |||
| GE_FUNC_VISIBILITY Status MallocSharedMemory(const TensorInfo &tensor_info, uint64_t &dev_addr, uint64_t &memory_size); | |||
| /// | |||
| /// \param var_name [in] var_name name of host variable. | |||
| /// \param base_addr [out] base_addr vase addr of host variable. | |||
| /// \param var_size [out] var_size memory_size of host variable. | |||
+ 0
- 35
inc/framework/omg/model_tool.h
View File
| @@ -1,35 +0,0 @@ | |||
| /** | |||
| * Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved. | |||
| * | |||
| * Licensed under the Apache License, Version 2.0 (the "License"); | |||
| * you may not use this file except in compliance with the License. | |||
| * You may obtain a copy of the License at | |||
| * | |||
| * http://www.apache.org/licenses/LICENSE-2.0 | |||
| * | |||
| * Unless required by applicable law or agreed to in writing, software | |||
| * distributed under the License is distributed on an "AS IS" BASIS, | |||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
| * See the License for the specific language governing permissions and | |||
| * limitations under the License. | |||
| */ | |||
| #ifndef INC_FRAMEWORK_OMG_MODEL_TOOL_H_ | |||
| #define INC_FRAMEWORK_OMG_MODEL_TOOL_H_ | |||
| #include <memory> | |||
| #include <string> | |||
| #include "framework/common/debug/ge_log.h" | |||
| #include "proto/ge_ir.pb.h" | |||
| namespace ge { | |||
| class GE_FUNC_VISIBILITY ModelTool { | |||
| public: | |||
| static Status GetModelInfoFromOm(const char *model_file, ge::proto::ModelDef &model_def, uint32_t &modeldef_size); | |||
| static Status GetModelInfoFromPbtxt(const char *model_file, ge::proto::ModelDef &model_def); | |||
| }; | |||
| } // namespace ge | |||
| #endif // INC_FRAMEWORK_OMG_MODEL_TOOL_H_ | |||
+ 1
- 0
inc/framework/omg/omg_inner_types.h
View File
| @@ -99,6 +99,7 @@ struct OmgContext { | |||
| std::string atc_cmdline; | |||
| bool user_attr_index_valid = false; | |||
| bool is_online_model = false; | |||
| bool is_subgraph_multi_batch = false; | |||
| }; | |||
| } // namespace ge | |||
+ 3
- 2
inc/framework/runtime/model_v2_executor.h
View File
| @@ -28,10 +28,10 @@ | |||
| #include "subscriber/executor_subscribers_scheduler.h" | |||
| namespace gert { | |||
| enum class ExecutorState { kInit, kLoaded }; | |||
| enum SubExeGraphType { kInitExeGraph, kMainExeGraph, kDeInitExeGraph, kSubExeGraphTypeEnd }; | |||
| static constexpr char *kSubExeGraphTypeStrs[kSubExeGraphTypeEnd] = { | |||
| const_cast<char *>("Init"), const_cast<char *>("Main"), const_cast<char *>("DeInit")}; | |||
| inline const char *GetSubExeGraphTypeStr(SubExeGraphType type) { | |||
| constexpr const char *kSubExeGraphTypeStrs[kSubExeGraphTypeEnd] = {"Init", "Main", "DeInit"}; | |||
| return kSubExeGraphTypeStrs[type]; | |||
| } | |||
| @@ -74,6 +74,7 @@ class VISIBILITY_EXPORT ModelV2Executor { | |||
| ModelDesc *model_desc_ = nullptr; | |||
| rtStream_t default_stream_ = nullptr; | |||
| ExecutorSubscribersScheduler subscribers_; | |||
| ExecutorState state_ = ExecutorState::kInit; | |||
| }; | |||
| } // namespace gert | |||
+ 1
- 2
inc/framework/runtime/subscriber/built_in_subscriber_definitions.h
View File
| @@ -32,8 +32,7 @@ enum class BuiltInSubscriberType { kProfiling, kDumper, kNum }; | |||
| enum class ProfilingType { | |||
| kHost, // 打开Host侧调度的profiling | |||
| kDevice, | |||
| kGeHost, // 打开GE Host侧调度的profiling | |||
| kSingleOpReg, // 单算子需要打开此开关开始register node name和kernel type | |||
| kGeHost, // 打开GE Host侧调度的profiling | |||
| kNum, | |||
| kAll = kNum | |||
| }; | |||
+ 1
- 4
inc/framework/runtime/subscriber/executor_subscribers_scheduler.h
View File
| @@ -23,9 +23,6 @@ | |||
| #include "global_profiling.h" | |||
| #include "framework/common/ge_visibility.h" | |||
| namespace gert { | |||
| namespace { | |||
| constexpr size_t kInitSubscriberSize = 1UL; | |||
| } | |||
| class ModelV2Executor; | |||
| class VISIBILITY_EXPORT ExecutorSubscribersScheduler { | |||
| public: | |||
| @@ -78,7 +75,7 @@ class VISIBILITY_EXPORT ExecutorSubscribersScheduler { | |||
| if (ins == nullptr) { | |||
| return nullptr; | |||
| } | |||
| constexpr size_t kInitSubscriberSize = 1UL; | |||
| // profiler exists when ess init | |||
| if (subscribers_.size() == kInitSubscriberSize) { | |||
| enabled_ = true; | |||
+ 5
- 0
inc/framework/runtime/subscriber/global_profiling.h
View File
| @@ -54,6 +54,11 @@ class GlobalProfiler { | |||
| class VISIBILITY_EXPORT GlobalProfilingWrapper { | |||
| public: | |||
| GlobalProfilingWrapper(const GlobalProfilingWrapper &) = delete; | |||
| GlobalProfilingWrapper(GlobalProfilingWrapper &&) = delete; | |||
| GlobalProfilingWrapper &operator=(const GlobalProfilingWrapper &) = delete; | |||
| GlobalProfilingWrapper &operator=(GlobalProfilingWrapper &&) = delete; | |||
| static GlobalProfilingWrapper *GetInstance() { | |||
| static GlobalProfilingWrapper global_prof_wrapper; | |||
| return &global_prof_wrapper; | |||
+ 1
- 1
metadef
| @@ -1 +1 @@ | |||
| Subproject commit 62c14e1cde161dccf6967f151ece9509f778c416 | |||
| Subproject commit 03482feb52fd7cc8544231f32891c86db3bc91a2 | |||
+ 4
- 4
third_party/fwkacllib/inc/cce/aicpu_engine_struct.h
View File
| @@ -1,5 +1,5 @@ | |||
| /** | |||
| * Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
| * Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved. | |||
| * | |||
| * Licensed under the Apache License, Version 2.0 (the "License"); | |||
| * you may not use this file except in compliance with the License. | |||
| @@ -14,8 +14,8 @@ | |||
| * limitations under the License. | |||
| */ | |||
| #ifndef AICPU_ENGINE_STRUCT_H__ | |||
| #define AICPU_ENGINE_STRUCT_H__ | |||
| #ifndef AICPU_ENGINE_STRUCT_H | |||
| #define AICPU_ENGINE_STRUCT_H | |||
| #include "fwk_adpt_struct.h" | |||
| @@ -53,4 +53,4 @@ struct SessionInfo { | |||
| #ifdef __cplusplus | |||
| } | |||
| #endif | |||
| #endif // AICPU_ENGINE_STRUCT_H__ | |||
| #endif // AICPU_ENGINE_STRUCT_H | |||
+ 5
- 5
third_party/fwkacllib/inc/cce/fwk_adpt_struct.h
View File
| @@ -1,5 +1,5 @@ | |||
| /** | |||
| * Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
| * Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved. | |||
| * | |||
| * Licensed under the Apache License, Version 2.0 (the "License"); | |||
| * you may not use this file except in compliance with the License. | |||
| @@ -14,8 +14,8 @@ | |||
| * limitations under the License. | |||
| */ | |||
| #ifndef FWK_ADPT_STRUCT_H__ | |||
| #define FWK_ADPT_STRUCT_H__ | |||
| #ifndef FWK_ADPT_STRUCT_H | |||
| #define FWK_ADPT_STRUCT_H | |||
| #include <cstdint> | |||
| @@ -112,7 +112,7 @@ struct StrFWKKernel { | |||
| }; | |||
| #pragma pack(pop) | |||
| typedef StrFWKKernel FWKOperateParam; | |||
| using FWKOperateParam = StrFWKKernel; | |||
| // Extent info ShapeAndType | |||
| const uint32_t kMaxShapeDims = 8U; | |||
| @@ -154,4 +154,4 @@ struct AsyncWait { | |||
| } // end namespace FWKAdapter | |||
| } // namespace aicpu | |||
| #endif // FWK_ADPT_STRUCT_H__ | |||
| #endif // FWK_ADPT_STRUCT_H | |||
+ 1
- 0
third_party/fwkacllib/inc/external/runtime/rt_error_codes.h
View File
| @@ -107,6 +107,7 @@ static const int32_t ACL_ERROR_RT_AICORE_TRAP_READ_OVERFLOW = 507042; // a | |||
| static const int32_t ACL_ERROR_RT_AICORE_TRAP_WRITE_OVERFLOW = 507043; // aic trap write overflow | |||
| static const int32_t ACL_ERROR_RT_VECTOR_CORE_TRAP_READ_OVERFLOW = 507044; // aiv trap read overflow | |||
| static const int32_t ACL_ERROR_RT_VECTOR_CORE_TRAP_WRITE_OVERFLOW = 507045; // aiv trap write overflow | |||
| static const int32_t ACL_ERROR_RT_STREAM_SYNC_TIMEOUT = 507046; // stream sync time out | |||
| static const int32_t ACL_ERROR_RT_DRV_INTERNAL_ERROR = 507899; // drv internal error | |||
| static const int32_t ACL_ERROR_RT_AICPU_INTERNAL_ERROR = 507900; // aicpu internal error | |||
+ 1
- 6
third_party/fwkacllib/inc/ops/OWNERS
View File
| @@ -21,7 +21,6 @@ reviewers: | |||
| - luanma_bl | |||
| - LDLD0524 | |||
| - wywismygod2020 | |||
| - lipeiyang3699 | |||
| - koala-zhang | |||
| - zhu-jingjing | |||
| - zhaozhihui5 | |||
| @@ -41,18 +40,14 @@ reviewers: | |||
| - djh602 | |||
| - wangjiangben_hw | |||
| - li1jie | |||
| - clinglai | |||
| - liujun2014 | |||
| - soupkey | |||
| - wu-shengji | |||
| - cimeng | |||
| - ccl_ligang | |||
| - xiaozhedeng | |||
| - granpad7 | |||
| - tc1qaz | |||
| - Ronnie_zheng | |||
| - xiexianhu | |||
| - zhouyujoe | |||
| - zhaoping12 | |||
| - tanshengshun | |||
| - fanqirui | |||
| @@ -62,4 +57,4 @@ reviewers: | |||
| - gegenhua | |||
| - qiaohairong | |||
| options: | |||
| no_parent_owners: true | |||
| no_parent_owners: true | |||
+ 1
- 1
third_party/fwkacllib/inc/ops/cluster.h
View File
| @@ -1,5 +1,5 @@ | |||
| /** | |||
| * Copyright 2021 Huawei Technologies Co., Ltd | |||
| * Copyright (c) Huawei Technologies Co., Ltd. 2020-2022. All rights reserved. | |||
| * | |||
| * Licensed under the Apache License, Version 2.0 (the "License"); | |||
| * you may not use this file except in compliance with the License. | |||
+ 2
- 2
third_party/fwkacllib/inc/ops/elewise_calculation_ops.h
View File
| @@ -125,7 +125,7 @@ REG_OP(MinimumGrad) | |||
| *@par Inputs: | |||
| *One input: | |||
| *x:A Tensor. Must be one of the following types: bool, float16, float, int8, int32, uint32, uint8, | |||
| int64, uint64, int16, uint16, double, complex64, complex128, qint8, quint8, qint16, quint16, qint32. | |||
| int64, uint64, int16, uint16, double, complex64, complex128, qint8, quint8, qint16, quint16, qint32, uint1. | |||
| For float32 type, the actual calculation on the chip is based on float16. \n | |||
| *@par Attributes: | |||
| @@ -137,7 +137,7 @@ REG_OP(MinimumGrad) | |||
| REG_OP(Cast) | |||
| .INPUT(x, TensorType({DT_BOOL, DT_FLOAT16, DT_FLOAT, DT_INT8, DT_INT32, DT_UINT32, DT_UINT8, | |||
| DT_INT64, DT_UINT64, DT_INT16, DT_UINT16, DT_DOUBLE, DT_COMPLEX64, | |||
| DT_COMPLEX128, DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32, DT_BF16})) | |||
| DT_COMPLEX128, DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32, DT_BF16, DT_UINT1})) | |||
| .OUTPUT(y, TensorType({DT_BOOL, DT_FLOAT16, DT_FLOAT, DT_INT8, DT_INT32, DT_UINT32, DT_UINT8, | |||
| DT_INT64, DT_UINT64, DT_INT16, DT_UINT16, DT_DOUBLE, DT_COMPLEX64, | |||
| DT_COMPLEX128, DT_QINT8, DT_QUINT8, DT_QINT16, DT_QUINT16, DT_QINT32, DT_BF16})) | |||
+ 140
- 0
third_party/fwkacllib/inc/ops/experiment_ops.h
View File
| @@ -77,6 +77,49 @@ REG_OP(ApplyAdamW) | |||
| .ATTR(maximize, Bool, false) | |||
| .OP_END_FACTORY_REG(ApplyAdamW) | |||
| /** | |||
| * @brief Calculate SQ distance. \n | |||
| * | |||
| * @par Inputs: | |||
| * @li ivf: A Tensor, dtype is uint8. | |||
| * @li query: A Tensor, dtype is float16 or float32. | |||
| * @li bucket_list: A Tensor, dtype is int32 or int64. | |||
| * @li bucket_limits: A Tensor, dtype is int32 or int64. | |||
| * @li bucket_offsets: A Tensor, dtype is int32 or int64. | |||
| * @li vmin: A Tensor, dtype is float16 or float32. | |||
| * @li vdiff: A Tensor, dtype is float16 or float32. \n | |||
| * | |||
| * @par Outputs: | |||
| * @li actual_count: A Tensor, dtype is int32 or int64, the actual number of sq_distance. | |||
| * @li sq_distance: A Tensor, dtype is float16 or float32. | |||
| * @li grouped_extreme_distance: A Tensor, dtype is float16 or float32, the extremum in each group of sq_distance. | |||
| * @li sq_ivf: A Tensor, dtype is int32 or int64. | |||
| * @li sq_index: A Tensor, dtype is int32 or int64. \n | |||
| * | |||
| * @par Attributes: | |||
| * @li total_limit: A Int, indicates the max length of the output sq_distance. | |||
| * @li group_size: A Int, indicates the group size of the extremum. | |||
| * @li extreme_mode: A Int, indicates the type of extremum, 0 means minimum, and 1 means maximum. \n | |||
| * | |||
| */ | |||
| REG_OP(ScanSQCodes) | |||
| .INPUT(ivf, TensorType({DT_UINT8})) | |||
| .INPUT(query, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
| .INPUT(bucket_list, TensorType({DT_INT32, DT_INT64})) | |||
| .INPUT(bucket_limits, TensorType({DT_INT32, DT_INT64})) | |||
| .INPUT(bucket_offsets, TensorType({DT_INT32, DT_INT64})) | |||
| .INPUT(vmin, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
| .INPUT(vdiff, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
| .OUTPUT(actual_count, TensorType({DT_INT32, DT_INT64})) | |||
| .OUTPUT(sq_distance, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
| .OUTPUT(grouped_extreme_distance, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
| .OUTPUT(sq_ivf, TensorType({DT_INT32, DT_INT64})) | |||
| .OUTPUT(sq_index, TensorType({DT_INT32, DT_INT64})) | |||
| .REQUIRED_ATTR(total_limit, Int) | |||
| .ATTR(group_size, Int, 64) | |||
| .ATTR(extreme_mode, Int, 0) | |||
| .OP_END_FACTORY_REG(ScanSQCodes) | |||
| /** | |||
| * @brief Multiplies matrix "a" by matrix "b", producing "a * b". \n | |||
| * @par Inputs: | |||
| @@ -153,6 +196,103 @@ REG_OP(RotatedNMS) | |||
| .OUTPUT(keep_indices, TensorType({DT_INT32, DT_INT64})) | |||
| .REQUIRED_ATTR(iou_threshold, Float) | |||
| .OP_END_FACTORY_REG(RotatedNMS) | |||
| /** | |||
| * @brief Performs average pooling on the input. Used in the combination of conv + avgpoolupdate to replace avgpool | |||
| * @par Inputs: | |||
| * x1: Output of upstream Conv2d. A tensor of type float16, float32. | |||
| * x2: Input feature map of upstream Conv2d. A tensor of type int8, float16, float32. | |||
| * @par Attributes: | |||
| * @li ksize: A required list of 4 ints, specifying the size (N, C, H, and W) of the sliding window, | |||
| * where N = C = 1, and H and W are positive integers within the range [1, 255]. | |||
| * @li strides: A required list of 4 ints, specifying the stride of the sliding window. | |||
| * The strides of the N and C dimensions are 1. | |||
| * The strides of the H and W dimensions are positive integers within the range [1, 63]. | |||
| * @li padding_mode: A required string, specifying the padding algorithm, | |||
| * either "VALID", "SAME" and "CALCULATED". | |||
| * With "SAME" means that the outputs will have the same spatial dimensions as its inputs. | |||
| * With "VALID" means no padding. | |||
| * @li pads: Pad value when padding_mode is "CALCULATED". | |||
| * @li data_format: An optional string, specifying the data format of "ksize" and "strides", | |||
| * either "NCHW", or "NHWC" (default). | |||
| * @li ceil_mode: Use ceil or floor to calculate the output size when padding_mode is "CALCULATED". | |||
| * @li exclusive: Ignore padding area or not when calculating average. | |||
| * @par Outputs: | |||
| * y: The average pooled output tensor. Has the same type and format as input "x1". | |||
| * @attention Constraints: | |||
| * @li Only single input and single output are supported. | |||
| * @li "ksize_H" and "ksize_W" are positive integers within the range [1, 255]. ksize_H * ksize_W < 256 | |||
| * @li Due to instruction restrictions, | |||
| * the values of "strides_h" and "strides_w" are positive integers within the range [1, 63]. | |||
| * @par Third-party framework compatibility | |||
| * Compatible with the TensorFlow/Pytorch/Onnx operator AvgPoolV2. | |||
| */ | |||
| REG_OP(AvgPoolUpdate) | |||
| .INPUT(x1, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
| .INPUT(x2, TensorType({DA_INT4, DT_INT8, DT_FLOAT16, DT_FLOAT})) | |||
| .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
| .REQUIRED_ATTR(ksize, ListInt) | |||
| .REQUIRED_ATTR(strides, ListInt) | |||
| .ATTR(padding_mode, String, "CALCULATED") | |||
| .ATTR(pads, ListInt, {0, 0, 0, 0}) | |||
| .ATTR(data_format, String, "NHWC") | |||
| .ATTR(ceil_mode, Bool, false) | |||
| .ATTR(exclusive, Bool, true) | |||
| .OP_END_FACTORY_REG(AvgPoolUpdate) | |||
| /** | |||
| * @brief batch input by time | |||
| * @par Inputs: | |||
| * x: A list of input tensors. It's a dynamic input | |||
| * @par Attributes: | |||
| * @li window: time window, [-1, int64_max], if -1 will batch by input data flag, | |||
| * else will batch by input timestamp and data flag. | |||
| * @li batch_dim: [-1, input_shape_range), if -1 input shape:[x, ..., x] ---> output shape:[-1, x, ..., x], | |||
| * else output shape:[x, ..., -1(batch_dim), ..., x]; | |||
| * @li drop_remainder: a bool flag, take effect when window > -1, | |||
| * if true when batch data window < window, will drop data. | |||
| * @par Outputs: | |||
| * y: A list of output tensors. It's a dynamic input, the same size as "x". | |||
| * @attention Constraints: | |||
| * @li Only support in helper udf | |||
| */ | |||
| REG_OP(TimeBatch) | |||
| .DYNAMIC_INPUT(x, TensorType::RealNumberType()) | |||
| .DYNAMIC_OUTPUT(y, TensorType::RealNumberType()) | |||
| .REQUIRED_ATTR(window, Int) | |||
| .ATTR(batch_dim, Int, -1) | |||
| .ATTR(drop_remainder, Bool, false) | |||
| .OP_END_FACTORY_REG(TimeBatch) | |||
| /** | |||
| * @brief Auto Batch process. \n | |||
| * @par Inputs: | |||
| * @li x: A list of input tensor objects. It's a dynamic input. \n | |||
| * @par Outputs: | |||
| * @li y: A list of output tensor objects. It's a dynamic output. \n | |||
| * @par Attributes: | |||
| * @li batch_size: auto batch size. | |||
| * @li timeout: auto batch wait timeout(unit:ms). | |||
| * @li padding: weather to pad when batch is insufficient. | |||
| * @li slide_stride: sliding window step. | |||
| */ | |||
| REG_OP(AutoBatch) | |||
| .DYNAMIC_INPUT(x, TensorType::RealNumberType()) | |||
| .DYNAMIC_OUTPUT(y, TensorType::RealNumberType()) | |||
| .REQUIRED_ATTR(batch_size, Int) | |||
| .ATTR(timeout, Int, 0) | |||
| .ATTR(padding, Bool, false) | |||
| .ATTR(slide_stride, Int, 0) | |||
| .OP_END_FACTORY_REG(AutoBatch) | |||
| } // namespace ge | |||
| #endif // OPS_BUILT_IN_OP_PROTO_INC_EXPERIMENT_OPS_H_ | |||
+ 14
- 14
third_party/fwkacllib/inc/ops/image_ops.h
View File
| @@ -2039,24 +2039,24 @@ REG_OP(IMGWarpOffsets) | |||
| .OP_END_FACTORY_REG(IMGWarpOffsets) | |||
| /** | |||
| *@brief This operation samples 3d input x by using interpolation based on flow field grid, | |||
| which is usually gennerated by affine_grid. | |||
| * @brief This operation samples 3d input x by using interpolation based on flow field grid, | |||
| which is usually gennerated by affine_grid. | |||
| *@par Inputs: | |||
| *@li x: 5-D Tensor with shape `[batch, channels, depth, height, width]`. | |||
| *@li grid: flow field grid, 5-D Tensor with shape `[batch, depth, height, width, 2]`. | |||
| * @par Inputs: | |||
| * @li x: 5-D Tensor with shape `[batch, channels, depth, height, width]`. | |||
| * @li grid: flow field grid, 5-D Tensor with shape `[batch, depth, height, width, 2]`. | |||
| *@par Attributes: | |||
| *@li interpolation_mode: An optional string specifying the interpolation method. | |||
| *@li padding_mode: An optional string specifying the pad method. | |||
| *@li align_corners: An optional bool. If "true", the centers of the corner | |||
| pixels of the input and output tensors are aligned. Defaults to "false" . | |||
| * @par Attributes: | |||
| * @li interpolation_mode: An optional string specifying the interpolation method. | |||
| * @li padding_mode: An optional string specifying the pad method. | |||
| * @li align_corners: An optional bool. If "true", the centers of the corner | |||
| pixels of the input and output tensors are aligned. Defaults to "false" . | |||
| *@par Outputs: | |||
| *y: Returns 5-D Tensor with the same dtype as `x`. | |||
| * @par Outputs: | |||
| * y: Returns 5-D Tensor with the same dtype as `x`. | |||
| *@par Third-party framework compatibility | |||
| *Compatible with pytorch GridSampler3D operator. | |||
| * @par Third-party framework compatibility | |||
| * Compatible with pytorch GridSampler3D operator. | |||
| */ | |||
| REG_OP(GridSampler3D) | |||
| .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) | |||
+ 1
- 1
third_party/fwkacllib/inc/ops/map_ops.h
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| @@ -88,7 +88,7 @@ REG_OP(TensorMapInsert) | |||
| * @par Inputs: | |||
| * @li input_handle: The input map. Must be type: DT_VARIANT. | |||
| * @li key: A Tensor,the key to be looked up. Must be one of | |||
| * @li key: A Tensor, the key to be looked up. Must be one of | |||
| the following types: int32,int64,string . \n | |||
| * @par Attributes: | |||
+ 4
- 4
third_party/fwkacllib/inc/ops/math_ops.h
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| @@ -674,7 +674,7 @@ REG_OP(Conj) | |||
| *@par Inputs: | |||
| *The input x and weight must have the same type. Inputs include: | |||
| *@li x: A Tensor dtype of float32. | |||
| *@li target: A Tensor dtype of int32. | |||
| *@li target: A Tensor dtype of int32 or int64. | |||
| *@li weight: A Tensor dtype of float32 . \n | |||
| *@par Attributes: | |||
| @@ -690,7 +690,7 @@ REG_OP(Conj) | |||
| */ | |||
| REG_OP(NLLLoss) | |||
| .INPUT(x, TensorType({DT_FLOAT})) | |||
| .INPUT(target, TensorType({DT_INT32})) | |||
| .INPUT(target, TensorType({DT_INT32, DT_INT64})) | |||
| .OPTIONAL_INPUT(weight, TensorType({DT_FLOAT})) | |||
| .OUTPUT(y, TensorType({DT_FLOAT})) | |||
| .OUTPUT(total_weight, TensorType({DT_FLOAT})) | |||
| @@ -704,7 +704,7 @@ REG_OP(NLLLoss) | |||
| *@par Inputs: | |||
| *@li x:A Tensor dtype of float32. | |||
| *@li y_grad:A Tensor dtype of float32. | |||
| *@li target:A Tensor dtype of int32. | |||
| *@li target:A Tensor dtype of int32, int64. | |||
| *@li weight:A Tensor dtype of float32. | |||
| *@li total_weight:A Tensor dtype of float32 . \n | |||
| @@ -721,7 +721,7 @@ REG_OP(NLLLoss) | |||
| REG_OP(NLLLossGrad) | |||
| .INPUT(x, TensorType({DT_FLOAT})) | |||
| .INPUT(y_grad, TensorType({DT_FLOAT})) | |||
| .INPUT(target, TensorType({DT_INT32})) | |||
| .INPUT(target, TensorType({DT_INT32, DT_INT64})) | |||
| .INPUT(weight, TensorType({DT_FLOAT})) | |||
| .INPUT(total_weight, TensorType({DT_FLOAT})) | |||
| .OUTPUT(x_grad, TensorType({DT_FLOAT})) | |||
+ 31
- 39
third_party/fwkacllib/inc/ops/matrix_calculation_ops.h
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| @@ -210,10 +210,10 @@ REG_OP(SwinTransformerLnQKV) | |||
| * float32, int32. Has format [ND, NHWC]. \n | |||
| * @par Attributes: | |||
| * @li transpose_x1: A bool. If True, changes the shape of "x1" from [M, K] to | |||
| * [K, M]. | |||
| * @li transpose_x2: A bool. If True, changes the shape of "x2" from [M, K] to | |||
| * [K, M]. \n | |||
| * @li transpose_x1: A bool. If True, changes the shape of "x1" from [K, M] to | |||
| * [M, K] before multiplication. | |||
| * @li transpose_x2: A bool. If True, changes the shape of "x2" from [N, K] to | |||
| * [K, N] before multiplication. \n | |||
| * @par Outputs: | |||
| * y: The result matrix Tensor. 2D. Must be one of the following types: float16, | |||
| @@ -246,9 +246,9 @@ REG_OP(MatMul) | |||
| * @par Attributes: | |||
| * @li transpose_x1: A bool. If True, changes the shape of "x1" from [K, M] to | |||
| * [M, K]. | |||
| * [M, K] before multiplication. | |||
| * @li transpose_x2: A bool. If True, changes the shape of "x2" from [N, K] to | |||
| * [K, N]. | |||
| * [K, N] before multiplication. | |||
| * @li offset_x: An optional integer for quantized MatMulV2. | |||
| * The negative offset added to the input x1 for int8 type. Ensure offset_x | |||
| * within the effective range of int8 [-128, 127]. Defaults to "0". \n | |||
| @@ -289,9 +289,9 @@ REG_OP(MatMulV2) | |||
| * @par Attributes: | |||
| * @li transpose_x1: A bool. If True, changes the shape of "x1" from [K, M] to | |||
| * [M, K]. | |||
| * [M, K] before multiplication. | |||
| * @li transpose_x2: A bool. If True, changes the shape of "x2" from [N, K] to | |||
| * [K, N]. | |||
| * [K, N] before multiplication. | |||
| * @li offset_x: An optional integer for quantized MatMulV2Compress. | |||
| * The negative offset added to the input x1 for int8 type. Ensure offset_x | |||
| * within the effective range of int8 [-128, 127]. Defaults to "0". \n | |||
| @@ -333,29 +333,20 @@ REG_OP(MatMulV2Compress) | |||
| * @li c: A matrix Tensor. Must be one of the following types:float32, float16, | |||
| * int8, int32. Has format ND. | |||
| * @li alpha: A 1D Tensor. The shape of alpha is [1].Must be one of the | |||
| * following types: float16, int32, float32, int8. Has format ND. | |||
| * following types: float32, float16, int8, int32. Has format ND. | |||
| * @li beta: A 1D Tensor. The shape of beta is [1]. Must be one of the following | |||
| * types: float16, int32, float32, int8. Has format ND.\n | |||
| * The format of a, b, c has restriction:\n | |||
| * When type of a is int8 and type of c is int32, the format of a, b, c should | |||
| * all be ND.\n | |||
| * When type of a is int8 and type of c is float32, the format of a, b, c | |||
| * should all be ND.\n | |||
| * When type of a is float16 and type of c is float16, the format of a, b, c | |||
| * should all be ND.\n | |||
| * When type of a is float16 and type of c is float32, the format of a, b, c | |||
| * should all be ND. \n | |||
| * types: float32, float16, int8, int32. Has format ND. \n | |||
| * @par Attributes: | |||
| * Two attributes, including: | |||
| * @li transpose_a: Optional. A bool. If True, changes the shape of "a" from | |||
| * [M, K] to [K, M]. | |||
| * [K, M] to [M, K] before multiplication. | |||
| * @li transpose_b: Optional. A bool. If True, changes the shape of "b" from | |||
| * [K, N] to [N, K]. \n | |||
| * [N, K] to [K, N] before multiplication. \n | |||
| * @par Outputs: | |||
| * y: The result matrix Tensor. Must be one of the following types: float16, | |||
| * float32, int32, int8. Has format [ND], the format should be equal to a. | |||
| * y: The result matrix Tensor. Must be one of the following types: float32, | |||
| * float16, int8, int32. Has format [ND], the format should be equal to a. | |||
| */ | |||
| REG_OP(GEMM) | |||
| @@ -379,10 +370,10 @@ REG_OP(GEMM) | |||
| * float32, int32. 2D or higher. Has format [ND, NHWC]. \n | |||
| * @par Attributes: | |||
| * @li adj_x1: A bool. If True, changes the shape of "x1" from [B, M, K] | |||
| * to [B, K, M]. | |||
| * @li adj_x2: A bool. If True, changes the shape of "x2" from [B, M, K] | |||
| * to [B, K, M]. \n | |||
| * @li adj_x1: A bool. If True, changes the shape of "x1" from [B, K, M] | |||
| * to [B, M, K] before multiplication. | |||
| * @li adj_x2: A bool. If True, changes the shape of "x2" from [B, N, K] | |||
| * to [B, K, N] before multiplication. \n | |||
| * @par Outputs: | |||
| * y: The result matrix Tensor. 2D or higher. Must be one of the following | |||
| @@ -418,10 +409,10 @@ REG_OP(BatchMatMul) | |||
| * int8, int4. Has format [ND, NHWC]. \n | |||
| * @par Attributes: | |||
| * @li adj_x1: A bool. If True, changes the shape of "x1" from [B, M, K] to | |||
| * [B, K, M]. | |||
| * @li adj_x2: A bool. If True, changes the shape of "x2" from [B, M, K] to | |||
| * [B, K, M]. \n | |||
| * @li adj_x1: A bool. If True, changes the shape of "x1" from [B, K, M] to | |||
| * [B, M, K] before multiplication. | |||
| * @li adj_x2: A bool. If True, changes the shape of "x2" from [B, N, K] to | |||
| * [B, K, N] before multiplication. \n | |||
| * @par Outputs: | |||
| * y: The result matrix Tensor. 2D or higher. Must be one of the following | |||
| @@ -784,7 +775,8 @@ REG_OP(TensorScatterUpdate) | |||
| * @par Attributes: | |||
| * @li axis: An optional attribute. Defaults to 0. | |||
| * @li reduction: An optional attribute. Defaults to string "none" and can be "add" or "mul". | |||
| * @li reduction: An optional attribute. Defaults to string "none" and can be | |||
| * "add" or "mul". \n | |||
| * @par Outputs: | |||
| * y: A Tensor. Has the same type and format as input "data" . \n | |||
| @@ -1147,7 +1139,7 @@ REG_OP(DiagPart) | |||
| * with a set of learned weights, and (optionally) adds biases. \n | |||
| * @par Inputs: | |||
| * Four inputs, including: | |||
| * @li x: A Tensor of type float16, int8, int4, float32. | |||
| * @li x: A Tensor of type float16, int8, int4. | |||
| * @li w: A weight matrix of type float16, int8, int4, float32. | |||
| * @li b: An optional Tensor of type float16, int32, float32. | |||
| * @li offset_w: An optional Tensor of type int8, int4. | |||
| @@ -1175,11 +1167,11 @@ REG_OP(DiagPart) | |||
| * Yes | |||
| */ | |||
| REG_OP(FullyConnection) | |||
| .INPUT(x, TensorType({DT_FLOAT16, DT_INT8, DT_INT4, DT_FLOAT32, DT_BF16})) | |||
| .INPUT(w, TensorType({DT_FLOAT16, DT_INT8, DT_INT4, DT_FLOAT32, DT_BF16})) | |||
| .OPTIONAL_INPUT(b, TensorType({DT_FLOAT16, DT_INT32,DT_FLOAT32, DT_BF16})) | |||
| .INPUT(x, TensorType({DT_FLOAT16, DT_INT8, DT_INT4, DT_FLOAT, DT_BF16})) | |||
| .INPUT(w, TensorType({DT_FLOAT16, DT_INT8, DT_INT4, DT_FLOAT, DT_BF16})) | |||
| .OPTIONAL_INPUT(b, TensorType({DT_FLOAT16, DT_INT32, DT_FLOAT, DT_BF16})) | |||
| .OPTIONAL_INPUT(offset_w, TensorType({DT_INT8, DT_INT4})) | |||
| .OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32,DT_FLOAT32, DT_BF16})) | |||
| .OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32, DT_FLOAT, DT_BF16})) | |||
| .REQUIRED_ATTR(num_output, Int) | |||
| .ATTR(transpose, Bool, false) | |||
| .ATTR(axis, Int, 1) | |||
| @@ -1643,7 +1635,7 @@ REG_OP(Tril) | |||
| * @par Inputs: | |||
| * @li x: A list of Tensors. Must be one of the following types: int32, | |||
| * float16, float32. Tensors to be concatenated. All must have size 1 in | |||
| * the first dimension and same shape.It's a dynamic input. \n | |||
| * the first dimension and same shape. It's a dynamic input. \n | |||
| * @par Attributes: | |||
| * @li equation: The subscripts for the Einstein summation. \n | |||
| @@ -1658,7 +1650,7 @@ REG_OP(Tril) | |||
| * Input N must be Int. \n | |||
| * @par Third-party framework compatibility | |||
| * Compatible with Pytorch einsum operator. | |||
| * Compatible with Tensorflow 2.x einsum operator. | |||
| */ | |||
| REG_OP(Einsum) | |||
| .DYNAMIC_INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32})) | |||
+ 2
- 2
third_party/fwkacllib/inc/ops/nn_batch_norm_ops.h
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| @@ -99,8 +99,8 @@ Specifies the variance used for inference. Must be "None" | |||
| if the operation is used for training . \n | |||
| *@par Attributes: | |||
| *@li epsilon: An optional float32, specifying the small value added to variance to avoid dividing by zero. | |||
| Defaults to "0.0001". | |||
| *@li epsilon: An optional float32, specifying the small value added to variance to avoid dividing by zero. | |||
| * Defaults to "0.0001". | |||
| *@li data_format: An optional string, specifying the format of "x". Defaults to "NHWC". | |||
| *@li is_training: An optional bool, specifying if the operation is used for training or inference. | |||
| Defaults to "True" . \n | |||
+ 54
- 65
third_party/fwkacllib/inc/ops/nn_calculation_ops.h
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| @@ -31,7 +31,7 @@ namespace ge { | |||
| * Three inputs include: | |||
| * @li input: 4D origin shape of input tensor [N, C, H, W] or [N, H, W, C], | |||
| * support float16. | |||
| * @li filter_size: A 4D tensor of type int32, int64, with shape [H, W, C, K] | |||
| * @li filter_size: A 4D tensor of type int32. | |||
| * @li out_backprop: 4D tensor with shape [N, C, H, W] or [N, H, W, C]. | |||
| * Must be one of the following types: float16. \n | |||
| @@ -52,9 +52,9 @@ namespace ge { | |||
| * @par Outputs: | |||
| * filter_grad: Gradient of the deep convolution relative to the filter with | |||
| * shape [H, W, C, K]. Must be one of the following types: float16. \n | |||
| * shape [H, W, C, K]. Must be one of the following types: float32. \n | |||
| * @attention Constraints:\n | |||
| * @attention Constraints: | |||
| * The feature map is 4D with shape [N, C, Hi, Wi] or [N, Hi, Wi, C], but | |||
| * the data is 5D with shape [N, C1, Hi, Wi, C0], where C0 is 16.\n | |||
| * The filter is 4D with shape [Hf, Wf, C, K], but the data is 6D with shape | |||
| @@ -90,7 +90,7 @@ REG_OP(DepthwiseConv2DBackpropFilter) | |||
| * Two inputs include: \n | |||
| * @li input: 4D tensor with shape [N, C, H, W] or [N, H, W, C], of type float16 | |||
| * @li out_backprop: 4D tensor with shape [N, C, H, W] or [N, H, W, C], | |||
| * of type float16 | |||
| * of type float16. | |||
| * @par Attributes: | |||
| * @li filter_size: A required list or tuple. Shape of filter. | |||
| @@ -133,8 +133,8 @@ REG_OP(DepthwiseConv2DBackpropFilter) | |||
| * instead. | |||
| */ | |||
| REG_OP(DepthwiseConv2DBackpropFilterD) | |||
| .INPUT(input, TensorType({DT_FLOAT16, DT_FLOAT32, DT_BF16})) | |||
| .INPUT(out_backprop, TensorType({DT_FLOAT16, DT_FLOAT32, DT_BF16})) | |||
| .INPUT(input, TensorType({DT_FLOAT16, DT_FLOAT, DT_BF16})) | |||
| .INPUT(out_backprop, TensorType({DT_FLOAT16, DT_FLOAT, DT_BF16})) | |||
| .OUTPUT(filter_grad, TensorType({DT_FLOAT32})) | |||
| .REQUIRED_ATTR(filter_size, ListInt) | |||
| .REQUIRED_ATTR(strides, ListInt) | |||
| @@ -147,9 +147,9 @@ REG_OP(DepthwiseConv2DBackpropFilterD) | |||
| * @brief Computes the gradients of depthwise convolution with respect to the | |||
| * input. \n | |||
| * @par Inputs: | |||
| * Three inputs include: \n | |||
| * Three inputs include: | |||
| * @li input_size: 4D shape of input tensor [N, C, H, W] or [N, H, W, C], | |||
| * support int32, int64. | |||
| * support int32. | |||
| * @li filter: 4D filter tensor with shape of [H, W, C, K], support float16. | |||
| * @li out_backprop: 4D tensor with shape [N, C, H, W] or [N, H, W, C]. | |||
| * Must be one of the following types: float16 . \n | |||
| @@ -172,7 +172,7 @@ REG_OP(DepthwiseConv2DBackpropFilterD) | |||
| * @par Outputs: | |||
| * input_grad: Gradient of the deep convolution relative to the input with shape | |||
| * [N, C, H, W] or [N, H, W, C] Must be one of the following types: | |||
| * float16, float32. \n | |||
| * float16. \n | |||
| * @attention Constraints:\n | |||
| * The feature map is 4D with shape [N, C, Hi, Wi] or [N, Hi, Wi, C], but | |||
| @@ -184,7 +184,7 @@ REG_OP(DepthwiseConv2DBackpropFilterD) | |||
| * data is 5D with shape [N, C1, Ho, Wo, C0], | |||
| * where C is the same as that of the feature map and C0 is 16.\n | |||
| * Limited by Tiling: max_h_in_l1 >= C0, where max_h_in_l1 = (l1_size - Hf * | |||
| * Wf * C0 * C0 * 2) / (2 * Wo *C0).\n | |||
| * Wf * C0 * C0 * 2) / (2 * Wo *C0). \n | |||
| * @par Third-party framework compatibility | |||
| * @li Compatible with the TensorFlow operator DepthwiseConv2DBackpropInput. | |||
| @@ -194,7 +194,7 @@ REG_OP(DepthwiseConv2DBackpropInput) | |||
| .INPUT(input_size, TensorType({DT_INT32, DT_INT64})) | |||
| .INPUT(filter, TensorType({DT_FLOAT16})) | |||
| .INPUT(out_backprop, TensorType({DT_FLOAT16})) | |||
| .OUTPUT(input_grad, TensorType({DT_FLOAT16, DT_FLOAT32})) | |||
| .OUTPUT(input_grad, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
| .REQUIRED_ATTR(strides, ListInt) | |||
| .ATTR(dilations, ListInt, {1, 1, 1, 1}) | |||
| .REQUIRED_ATTR(pads, ListInt) | |||
| @@ -355,9 +355,8 @@ REG_OP(BiasAddGrad) | |||
| * data tensor. An integer vector representing the shape of input, where | |||
| * input is a 4-D tensor [batch, height, width, channels] | |||
| * or [batch, channels, height, width]. | |||
| * @li filter: A Tensor. Must be one of the following types: float16, float32, | |||
| * float64. 4-D with shape | |||
| * [filter_height, filter_width, in_channels, out_channels] | |||
| * @li filter: A Tensor. Must be one of the following types: float16. | |||
| * 4-D with shape [filter_height, filter_width, in_channels, out_channels] | |||
| * or [out_channels, filter_height, filter_width, in_channels] | |||
| * or [out_channels, in_channel, filter_height, filter_width]. | |||
| * @li out_backprop: A Tensor. Must have the same type as filter. | |||
| @@ -372,14 +371,9 @@ REG_OP(BiasAddGrad) | |||
| | Tensor | out_bckprop | filter | y |\n | |||
| |-----------|-------------|---------|--------|\n | |||
| | Data Type | float16 | float16 | float16|\n | |||
| | | float32 | float32 | float32|\n | |||
| | | float64 | float64 | float64|\n | |||
| | Format | NCHW | NCHW | NCHW |\n | |||
| | | NHWC | HWCN | NHWC |\n | |||
| *\n | |||
| * For float32 and float64 type, the actual calculation on the chip is based | |||
| * on float16. | |||
| *\n | |||
| * | |||
| *@par Attributes: | |||
| * Five attributes: | |||
| @@ -400,13 +394,13 @@ REG_OP(BiasAddGrad) | |||
| *\n | |||
| | Name | Field | Scope |\n | |||
| |------------------|----------|--------------|\n | |||
| | input_size | H | [1, 200000] |\n | |||
| | input_size | H | [1, 4096] |\n | |||
| | | W | [1, 4096] |\n | |||
| | Filter | H | [1, 255] |\n | |||
| | | W | [1, 255] |\n | |||
| | out_backprop | H*strideH| [1, 200000] |\n | |||
| | out_backprop | H*strideH| [1, 4096] |\n | |||
| | | W*strideW| [1, 4096] |\n | |||
| | y(fmap) | H | [1, 200000] |\n | |||
| | y(fmap) | H | [1, 4096] |\n | |||
| | | W | [1, 4096] |\n | |||
| | Stride | H | [1, 63] |\n | |||
| | | W | [1, 63] |\n | |||
| @@ -455,7 +449,7 @@ REG_OP(Conv2DBackpropInput) | |||
| *@brief Computes the gradients of convolution with respect to the input. | |||
| * @par Inputs: | |||
| * Two inputs: | |||
| * @li filter: A Tensor. Types is float16. | |||
| * @li filter: A Tensor. Types is float16 or int8. | |||
| * 4-D with shape [filter_height, filter_width, in_channels, out_channels] | |||
| * or [out_channels, filter_height, filter_width, in_channels] | |||
| * or [out_channels, in_channel, filter_height, filter_width]. | |||
| @@ -479,8 +473,8 @@ REG_OP(Conv2DBackpropInput) | |||
| * @li data_format: An optional string from: "NHWC", "NCHW". Defaults to | |||
| * "NHWC". Specify the data format of the input and output data. | |||
| *@par Outputs: | |||
| * y: A Tensor. Has the same type as filter,4-D tensor [batch, height, width, | |||
| * channels] or [batch, channels, height, width]. | |||
| * y: A Tensor. with the type of: float16, float32, int32, 4-D tensor | |||
| * [batch, height, width, channels] or [batch, channels, height, width]. | |||
| * @par Third-party framework compatibility | |||
| * Compatible with Tensorflow's conv2d_backprop_input | |||
| *@par Restrictions: | |||
| @@ -547,11 +541,11 @@ REG_OP(Conv2DBackpropInputD) | |||
| *\n | |||
| | Name | Field | Scope |\n | |||
| |------------------|----------|--------------|\n | |||
| | x (out_backprop) | H*strideH| [1, 200000] |\n | |||
| | x (out_backprop) | H*strideH| [1, 4096] |\n | |||
| | | W*strideW| [1, 4096] |\n | |||
| | Filter | H | [1, 255] |\n | |||
| | | W | [1, 255] |\n | |||
| | y (fmap) | H | [1, 200000] |\n | |||
| | y (fmap) | H | [1, 4096] |\n | |||
| | | W | [1, 4096] |\n | |||
| | Stride | H | [1, 63] |\n | |||
| | | W | [1, 63] |\n | |||
| @@ -602,8 +596,8 @@ REG_OP(Deconvolution) | |||
| *@brief Computes the gradients of convolution with respect to the filter | |||
| *@par Inputs: | |||
| * Three inputs: | |||
| * @li x: A Tensor. Must be one of the following types: float16, float32, | |||
| * float64. 4-D with shape [batch, in_height, in_width, in_channels] or | |||
| * @li x: A Tensor. Must be one of the following types: float16. | |||
| * 4-D with shape [batch, in_height, in_width, in_channels] or | |||
| * [batch, in_channels, in_height, in_width]. | |||
| * @li filter_size: A const Tensor of type int32. Currently does not support | |||
| * data tensor. An integer vector representing the tensor shape of filter, | |||
| @@ -621,9 +615,7 @@ REG_OP(Deconvolution) | |||
| *\n | |||
| | Tensor | x | out_backprop | y |\n | |||
| |-----------|---------|--------------|---------|\n | |||
| | Data Type | float16 | float16 | float16 |\n | |||
| | | float32 | float32 | float32 |\n | |||
| | | float64 | float64 | float64 |\n | |||
| | Data Type | float16 | float16 | float32 |\n | |||
| | Format | NCHW | NCHW | NCHW |\n | |||
| | | NHWC | NHWC | HWCN |\n | |||
| *\n | |||
| @@ -650,13 +642,13 @@ REG_OP(Deconvolution) | |||
| *\n | |||
| | Name | Field | Scope |\n | |||
| |------------------|----------|--------------|\n | |||
| | x(fmap) | H | [1, 200000] |\n | |||
| | x(fmap) | H | [1, 4096] |\n | |||
| | | W | [1, 4096] |\n | |||
| | Filter Size | H | [1, 255] |\n | |||
| | | W | [1, 255] |\n | |||
| | out_backprop | H | [1, 200000] |\n | |||
| | out_backprop | H | [1, 4096] |\n | |||
| | | W | [1, 4096] |\n | |||
| | y | H | [1, 200000] |\n | |||
| | y | H | [1, 4096] |\n | |||
| | | W | [1, 4096] |\n | |||
| | Stride | H | [1, 63] |\n | |||
| | | W | [1, 63] |\n | |||
| @@ -1015,13 +1007,12 @@ REG_OP(DeformableConv2D) | |||
| /** | |||
| *@brief Computes a 3D convolution given 5D "x" and "filter" tensors. | |||
| *@par Inputs: | |||
| * @li x: A 5D tensor. Must be one of the following types: float16, | |||
| * (Currently does not support int8). The format of x is NCDHW or NDHWC. | |||
| * @li x: A 5D tensor. Must be one of the following types: float16, int8. | |||
| * The format of x is NCDHW or NDHWC. | |||
| * @li filter: A 5D tensor of the same type as "x". | |||
| * (Currently does not support int8). | |||
| * The format is NCDHW, NDHWC or DHWCN. | |||
| * @li bias: Optional. An 1D tensor of the same type as "x". | |||
| * @li offset_w: Optional. An 1D tensor for quantized deconvolution. Reserved. \n | |||
| * @li offset_w: Optional. An 1D tensor for quantized deconvolution. \n | |||
| *@par Attributes: | |||
| * @li strides: Required. A list of 5 integers. Specifies the stride of the | |||
| @@ -1041,7 +1032,8 @@ REG_OP(DeformableConv2D) | |||
| * Defaults to 0. Reserved. \n | |||
| *@par Outputs: | |||
| * y: A Tensor. Has the same type and data format as "x". \n | |||
| * y: A Tensor. Has the same data format as "x". if the type of "x" is int8, | |||
| * the type of y is int32. \n | |||
| *@attention Constraints: | |||
| * The image size after padding is greater than the filter size. \n | |||
| @@ -1051,11 +1043,11 @@ REG_OP(DeformableConv2D) | |||
| * @li Compatible with the Caffe operator Convolution. | |||
| */ | |||
| REG_OP(Conv3D) | |||
| .INPUT(x, TensorType({DT_FLOAT16})) | |||
| .INPUT(filter, TensorType({DT_FLOAT16})) | |||
| .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_FLOAT32})) | |||
| .INPUT(x, TensorType({DT_FLOAT16, DT_INT8})) | |||
| .INPUT(filter, TensorType({DT_FLOAT16, DT_INT8})) | |||
| .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_FLOAT32, DT_INT32})) | |||
| .OPTIONAL_INPUT(offset_w, TensorType({DT_INT8})) | |||
| .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT32})) | |||
| .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT32, DT_INT32})) | |||
| .REQUIRED_ATTR(strides, ListInt) | |||
| .REQUIRED_ATTR(pads, ListInt) | |||
| .ATTR(dilations, ListInt, {1, 1, 1, 1, 1}) | |||
| @@ -1068,12 +1060,11 @@ REG_OP(Conv3D) | |||
| /** | |||
| *@brief Computes the gradients of convolution 3d with respect to the input. | |||
| *@par Inputs: | |||
| * @li input_size: A Tensor of type int32, int64. An integer vector | |||
| * @li input_size: A Tensor of type int32. An integer vector | |||
| * representing the shape of input, where input is a 5-D tensor | |||
| * [batch, depth, height, width, channels] or | |||
| * [batch, channels, depth, height, width]. | |||
| * @li filter: A Tensor. Must be one of the following types: float16, float32. | |||
| * Currently does not support double. | |||
| * @li filter: A Tensor. Must be one of the following types: float16. | |||
| * @li out_backprop: A Tensor. Must have the same type as filter. | |||
| * 5-D with shape [batch, depth, out_height, out_width, out_channels] | |||
| * or [batch, out_channels, depth, out_height, out_width]. Gradients with | |||
| @@ -1095,8 +1086,7 @@ REG_OP(Conv3D) | |||
| * Defaults to "NDHWC". Specify the data format of the input and output data. \n | |||
| *@par Outputs: | |||
| * y: A Tensor. Has the same type as filter,and has same format as | |||
| * "input_size". \n | |||
| * y: A Tensor. Has same format as "input_size". \n | |||
| *@par Third-party framework compatibility | |||
| * Compatible with Tensorflow's conv3d_backprop_input | |||
| @@ -1207,8 +1197,7 @@ REG_OP(LSTM) | |||
| /** | |||
| *@brief Computes the gradients of convolution3D with respect to the filter | |||
| *@par Inputs: | |||
| * @li x: A Tensor. Must be one of the following types: float16, float32, | |||
| * double. Currently does not support double. | |||
| * @li x: A Tensor. Must be one of the following types: float16. | |||
| * 5-D with shape [batch, in_depth, in_height, in_width, in_channels] | |||
| * or [batch, in_channels, in_depth, in_height, in_width]. | |||
| * @li filter_size: A Tensor of type int32. An integer vector representing the | |||
| @@ -1236,7 +1225,7 @@ REG_OP(LSTM) | |||
| * Defaults to "NDHWC". Specify the data format of the input and output data. \n | |||
| *@par Outputs: | |||
| * y: A Tensor that has the same type as "x" and the format is NDHWC, NCDHW | |||
| * y: A Tensor that has the type float32 and the format is NDHWC, NCDHW | |||
| * or DHWCN. \n | |||
| *@par Third-party framework compatibility | |||
| @@ -1310,7 +1299,7 @@ REG_OP(Conv3DBackpropFilterD) | |||
| *@brief Computes the transpose of convolution 3d with respect to the input. | |||
| *@par Inputs: | |||
| * @li input_size: A Tensor of type int32, int64. An integer vector | |||
| * @li input_size: A Tensor of type int32. An integer vector | |||
| * representing the shape of input. | |||
| * @li x: A Tensor of type float16, currently does not support int8. The format | |||
| * is NDHWC or NCDHW. | |||
| @@ -1336,7 +1325,7 @@ REG_OP(Conv3DBackpropFilterD) | |||
| * @li offset_x: Optional. Input offset_x value. Reserved. \n | |||
| *@par Outputs: | |||
| * y: A Tensor. Has the same type and format as "x". | |||
| * y: A Tensor. Has the same format as "x", has the type float16, float32. | |||
| */ | |||
| REG_OP(Conv3DTranspose) | |||
| .INPUT(input_size, TensorType({DT_INT32, DT_INT64})) | |||
| @@ -1362,7 +1351,7 @@ REG_OP(Conv3DTranspose) | |||
| * The format is NDHWC or NCDHW. | |||
| * @li filter: A Tensor of type float16, currently does not support int8. | |||
| * The format is NDHWC, NCDHW or DHWCN. | |||
| * @li bias: Optional. An 1D tensor of the same type as "x". Reserved. | |||
| * @li bias: Optional. An 1D tensor of the same type as "x". | |||
| * @li offset_w: Optional. An 1D tensor for quantized deconvolution. Reserved. \n | |||
| *@par Attributes: | |||
| @@ -1383,7 +1372,7 @@ REG_OP(Conv3DTranspose) | |||
| * @li offset_x: Optional. Input offset_x value. Reserved. \n | |||
| *@par Outputs: | |||
| * y: A Tensor. Has the same type and format as "x". \n | |||
| * y: A Tensor. Has the same format as "x", has the type float16, float32. \n | |||
| *@par Restrictions: | |||
| * Warning: THIS FUNCTION IS DEPRECATED. Please use Conv3DTranspose instead. | |||
| @@ -1428,7 +1417,7 @@ REG_OP(Conv3DTransposeD) | |||
| | Tensor | x | filter | bias | y |\n | |||
| |-----------|---------|---------|---------|--------|\n | |||
| | Data Type | float16 | float16 | float16 | float16|\n | |||
| | | int8 | int8 | int32 | int32 |\n | |||
| | | float16 | float16 | float32 | float32|\n | |||
| | Format | NCHW | NCHW | ND | NCHW |\n | |||
| | | NHWC | HWCN | | NHWC |\n | |||
| *\n | |||
| @@ -1461,13 +1450,13 @@ REG_OP(Conv3DTransposeD) | |||
| *\n | |||
| | Name | Field | Scope |\n | |||
| |------------------|----------|--------------|\n | |||
| | input_size | H | [1, 200000] |\n | |||
| | input_size | H | [1, 4096] |\n | |||
| | | W | [1, 4096] |\n | |||
| | x (out_backprop) | H*strideH| [1, 200000] |\n | |||
| | x (out_backprop) | H*strideH| [1, 4096] |\n | |||
| | | W*strideW| [1, 4096] |\n | |||
| | filter | H | [1, 255] |\n | |||
| | | W | [1, 255] |\n | |||
| | y (fmap) | H | [1, 200000] |\n | |||
| | y (fmap) | H | [1, 4096] |\n | |||
| | | W | [1, 4096] |\n | |||
| | Stride | H | [1, 63] |\n | |||
| | | W | [1, 63] |\n | |||
| @@ -1503,9 +1492,9 @@ REG_OP(Conv2DTranspose) | |||
| .INPUT(input_size, TensorType({DT_INT32, DT_INT64})) | |||
| .INPUT(x, TensorType({DT_FLOAT16, DT_INT8})) | |||
| .INPUT(filter, TensorType({DT_FLOAT16, DT_INT8})) | |||
| .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_INT32, DT_FLOAT32})) | |||
| .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_INT32, DT_FLOAT})) | |||
| .OPTIONAL_INPUT(offset_w, TensorType({DT_INT8})) | |||
| .OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32, DT_FLOAT32})) | |||
| .OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32, DT_FLOAT})) | |||
| .REQUIRED_ATTR(strides, ListInt) | |||
| .REQUIRED_ATTR(pads, ListInt) | |||
| .ATTR(dilations, ListInt, {1, 1, 1, 1}) | |||
| @@ -1522,7 +1511,7 @@ REG_OP(Conv2DTranspose) | |||
| * @li x: A Tensor of type float16, int8. | |||
| * @li filter: A Tensor of type float16, int8. Must have the same type as "x". | |||
| * @li bias: An optional 1D tensor of the same type as "x". | |||
| * @li offset_w: An optional 1D tensor for quantized inference. Type is int8. Reserved. | |||
| * @li offset_w: An optional 1D tensor for quantized inference. Type is int8. | |||
| *@par Required Attributes: | |||
| * @li input_size: A Tensor of type int32 or int64. An integer vector representing the | |||
| * shape of input. | |||
| @@ -1550,9 +1539,9 @@ REG_OP(Conv2DTranspose) | |||
| REG_OP(Conv2DTransposeD) | |||
| .INPUT(x, TensorType({DT_FLOAT16, DT_INT8})) | |||
| .INPUT(filter, TensorType({DT_FLOAT16, DT_INT8})) | |||
| .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_INT32, DT_FLOAT32})) | |||
| .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_INT32, DT_FLOAT})) | |||
| .OPTIONAL_INPUT(offset_w, TensorType({DT_INT8})) | |||
| .OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32, DT_FLOAT32})) | |||
| .OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32, DT_FLOAT})) | |||
| .REQUIRED_ATTR(input_size, ListInt) | |||
| .REQUIRED_ATTR(strides, ListInt) | |||
| .REQUIRED_ATTR(pads, ListInt) | |||
+ 107
- 0
third_party/fwkacllib/inc/ops/nn_norm_ops.h
View File
| @@ -193,6 +193,39 @@ REG_OP(SigmoidCrossEntropyWithLogitsV2) | |||
| .ATTR(reduction, String, "mean") | |||
| .OP_END_FACTORY_REG(SigmoidCrossEntropyWithLogitsV2) | |||
| /** | |||
| * @brief Computes the sigmoid focal loss of "pred" and "target". | |||
| * @par Inputs: | |||
| * Three inputs, including: | |||
| * @li pred: A 2-dimensional Tensor of type float16 or float32, specifying the predicted value. | |||
| * @li target: A 1-dimensional Tensor of type int32, specifying the target value. | |||
| * @li weight: A 1-dimensional Tensor, specifying the weight value. \n | |||
| * @par Attributes: | |||
| * @li gamma: An optional float, specifying the exponent of the modulating factor (1 - pt) | |||
| * to balance easy/hard examples. Defaults to 2.0. | |||
| * @li alpha: An optional float, specifying the weighting factor in range (1, 0) to balance | |||
| * the importance of positive/negative examples or less than 0 for ignore. Defaults to 0.25. | |||
| * @li reduction: A optional character string from "none", "mean", and "sum", specifying the | |||
| * reduction type to be applied to the output. Defaults to "mean". \n | |||
| * @par Outputs: | |||
| * loss: Sigmoid focal loss between the predicted value and target value. Has the same dimensions as "pred". \n | |||
| * @par Third-party framework compatibility | |||
| * Compatible with mmcv operator SigmoidFocalLoss. | |||
| */ | |||
| REG_OP(SigmoidFocalLoss) | |||
| .INPUT(pred, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
| .INPUT(target, TensorType({DT_INT32})) | |||
| .OPTIONAL_INPUT(weight, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
| .OUTPUT(loss, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
| .ATTR(gamma, Float, 2.0) | |||
| .ATTR(alpha, Float, 0.25) | |||
| .ATTR(reduction, String, "mean") | |||
| .OP_END_FACTORY_REG(SigmoidFocalLoss) | |||
| /** | |||
| * @brief Computes the regression box of the RPN. It is a FasterRCNN operator . | |||
| @@ -1834,5 +1867,79 @@ REG_OP(AxpyWithSoftmaxAndDropOutDoMask) | |||
| .REQUIRED_ATTR(input_keep_prob, Float) | |||
| .ATTR(axis, ListInt, {-1}) | |||
| .OP_END_FACTORY_REG(AxpyWithSoftmaxAndDropOutDoMask) | |||
| /** | |||
| * @brief MMCV Function: sigmoid_focal_loss_grad . \n | |||
| * @par Inputs: | |||
| * Three inputs, including: | |||
| * @li pred: the predicted tensor. The type support float16 and float32. | |||
| * @li target: the target label Tensor. The type support Int32. | |||
| * @li dout: the grad of previous op grad, which has the sampe shape wth pred. The type support float16 and float32. | |||
| * @li weight: A optioanl input Tensor, default is None, which helps to calculate the loss by supplying sample weights: | |||
| * shape of pred should be (B，D), B means batch size, D means the number of labels. | |||
| * shape of target should be (D, ). | |||
| * shape of weight should be (D, ) \n | |||
| * @par Attributes: | |||
| * @li alpha: A attribute is used to reweight the sample. The type is float . \n | |||
| * @li gamma: A attribute is used to calculate the power of the probability. | |||
| * The type is float . \n | |||
| * @li reduction: a type of the reduce method. default is 'mean', which means computing the average loss. | |||
| 'sum' means computing the sum of the loss, 'none' means no reducing .\n | |||
| * @par Outputs: | |||
| * grad: A mutable Tensor. Has the same type and shape as "pred". \n | |||
| * @par Third-party framework compatibility | |||
| * Compatible with the MMCV operator SigmoidFocalLoss. | |||
| */ | |||
| REG_OP(SigmoidFocalLossGrad) | |||
| .INPUT(pred, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
| .INPUT(target, TensorType({DT_INT32})) | |||
| .INPUT(dout, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
| .OPTIONAL_INPUT(weight, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
| .OUTPUT(grad, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
| .ATTR(alpha, Float, 0.25) | |||
| .ATTR(gamma, Float, 2.0) | |||
| .ATTR(reduction, String, "mean") | |||
| .OP_END_FACTORY_REG(SigmoidFocalLossGrad) | |||
| /** | |||
| * @brief MMCV Function: softmax_focal_loss_grad . \n | |||
| * @par Inputs: | |||
| * Three inputs, including: | |||
| * @li pred: the predicted tensor. The type support float16 and float32. | |||
| * @li target: the target label Tensor. The type support Int32. | |||
| * @li dout: the grad of previous op grad, which has the sampe shape wth pred. The type support float16 and float32. | |||
| * @li weight: A optioanl input Tensor, default is None, which helps to calculate the loss by supplying sample weights: | |||
| * shape of pred should be (B，D), B means batch size, D means the number of labels. | |||
| * shape of target should be (B, D). | |||
| * shape of weight should be (D, ) \n | |||
| * @par Attributes: | |||
| * @li alpha: A attribute is used to reweight the sample. The type is float . \n | |||
| * @li gamma: A attribute is used to calculate the power of the probability. | |||
| * The type is float . \n | |||
| * @li reduction: a type of the reduce method. default is 'mean', which means computing the average loss. | |||
| 'sum' means computing the sum of the loss, 'none' means no reducing .\n | |||
| * @par Outputs: | |||
| * grad: A mutable Tensor. Has the same type and shape as "pred". \n | |||
| * @par Third-party framework compatibility | |||
| * Compatible with the MMCV operator SoftmaxFocalLossGrad. | |||
| */ | |||
| REG_OP(SoftmaxFocalLossGrad) | |||
| .INPUT(pred, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
| .INPUT(target, TensorType({DT_INT32})) | |||
| .INPUT(dout, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
| .OPTIONAL_INPUT(weight, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
| .OUTPUT(grad, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
| .ATTR(alpha, Float, 0.25) | |||
| .ATTR(gamma, Float, 2.0) | |||
| .ATTR(reduction, String, "mean") | |||
| .OP_END_FACTORY_REG(SoftmaxFocalLossGrad) | |||
| } // namespace ge | |||
| #endif // OPS_BUILT_IN_OP_PROTO_INC_NN_NORM_OPS_H_ | |||
+ 23
- 23
third_party/fwkacllib/inc/ops/nn_pooling_ops.h
View File
| @@ -174,8 +174,8 @@ REG_OP(AvgPoolV2) | |||
| /** | |||
| * @brief Performs average pooling on the input. \n | |||
| * @par Inputs: | |||
| * x: A 5-D Tensor of shape [batch, depth, height, width, channels] and type | |||
| * float16, float32, double. \n | |||
| * @li x: A 5-D Tensor of shape [batch, depth, height, width, channels] and | |||
| * type float16. \n | |||
| * @par Attributes: | |||
| * @li ksize: List of ints that has length 1, 3 or 5. The size of the window | |||
| @@ -201,8 +201,8 @@ REG_OP(AvgPoolV2) | |||
| * Compatible with the TensorFlow operator AvgPool3D. | |||
| */ | |||
| REG_OP(AvgPool3D) | |||
| .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
| .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
| .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) | |||
| .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) | |||
| .REQUIRED_ATTR(ksize, ListInt) | |||
| .REQUIRED_ATTR(strides, ListInt) | |||
| .REQUIRED_ATTR(pads, ListInt) | |||
| @@ -216,9 +216,9 @@ REG_OP(AvgPool3D) | |||
| /** | |||
| * @brief Performs average pooling on the input. | |||
| * @par Inputs: | |||
| * @li x: A 5-D Tensor of shape [batch, depth, height, width, channels] and type float16, float32, double. | |||
| * @li filter: An optional tensor of type float16, float32, double, fractal_z_3d layout. | |||
| * @li multiplier: An optional tensor of float16, float32, double. | |||
| * @li x: A 5-D Tensor of shape [batch, depth, height, width, channels] and type float16. | |||
| * @li filter: An optional tensor of type float16, fractal_z_3d layout. | |||
| * @li multiplier: An optional tensor of float16. | |||
| * @par Attributes: | |||
| * @li ksize: List of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor. | |||
| @@ -239,10 +239,10 @@ REG_OP(AvgPool3D) | |||
| * Compatible with the TensorFlow operator AvgPool3D. | |||
| */ | |||
| REG_OP(AvgPool3DD) | |||
| .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
| .OPTIONAL_INPUT(filter, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
| .OPTIONAL_INPUT(multiplier, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
| .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
| .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) | |||
| .OPTIONAL_INPUT(filter, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) | |||
| .OPTIONAL_INPUT(multiplier, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) | |||
| .OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT, DT_DOUBLE})) | |||
| .REQUIRED_ATTR(ksize, ListInt) | |||
| .REQUIRED_ATTR(strides, ListInt) | |||
| .REQUIRED_ATTR(pads, ListInt) | |||
| @@ -256,7 +256,7 @@ REG_OP(AvgPool3DD) | |||
| * @brief Computes AvgPool3DGrad function. \n | |||
| * @par Inputs: | |||
| * @li orig_input_shape: An NDHWC tensor of type int32. | |||
| * @li grads: An NDHWC tensor of type float16, float32, or double. \n | |||
| * @li grads: An NDHWC tensor of type float16. \n | |||
| * @par Attributes: | |||
| * @li ksize: List of ints that has length 5. The size of the window for | |||
| @@ -284,8 +284,8 @@ REG_OP(AvgPool3DD) | |||
| REG_OP(AvgPool3DGrad) | |||
| .INPUT(orig_input_shape, TensorType({DT_INT32})) | |||
| .INPUT(grads, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
| .OUTPUT(output, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
| .INPUT(grads, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) | |||
| .OUTPUT(output, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) | |||
| .REQUIRED_ATTR(ksize, ListInt) | |||
| .REQUIRED_ATTR(strides, ListInt) | |||
| .REQUIRED_ATTR(pads, ListInt) | |||
| @@ -299,7 +299,7 @@ REG_OP(AvgPool3DGrad) | |||
| * @brief Performs average pooling on the input. | |||
| * @par Inputs: | |||
| * @li grads: An NDHWC tensor of type float16. | |||
| * @li filter: An optional tensor of type float16, fractal_z_3d layout. | |||
| * @li filter: An optional tensor of type float16. | |||
| * @li multiplier: An optional tensor of float16. | |||
| * @par Attributes: | |||
| @@ -867,8 +867,8 @@ REG_OP(MaxPoolGradGradWithArgmax) | |||
| /** | |||
| * @brief Computes avgpoograd function. \n | |||
| * @par Inputs: | |||
| * @li orig_input_shape: An NHWC tensor of type int32. | |||
| * @li input_grad: An NHWC tensor of type float16, float32, or double. \n | |||
| * @li orig_input_shape: A tensor of type int32. | |||
| * @li input_grad: A tensor of type float16. \n | |||
| * @par Attributes: | |||
| * @li ksize: A required tuple or list, specifying the size of the window for | |||
| @@ -887,8 +887,8 @@ REG_OP(MaxPoolGradGradWithArgmax) | |||
| */ | |||
| REG_OP(AvgPoolGrad) | |||
| .INPUT(orig_input_shape, TensorType({DT_INT32})) | |||
| .INPUT(input_grad, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
| .OUTPUT(out_grad, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
| .INPUT(input_grad, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) | |||
| .OUTPUT(out_grad, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) | |||
| .REQUIRED_ATTR(ksize, ListInt) | |||
| .REQUIRED_ATTR(strides, ListInt) | |||
| .REQUIRED_ATTR(padding, String) | |||
| @@ -898,9 +898,9 @@ REG_OP(AvgPoolGrad) | |||
| /** | |||
| * @brief Computes gradients of average pooling function . \n | |||
| * @par Inputs: | |||
| * @input_grad: An NHWC tensor of type float16. | |||
| * @mean_matrix: Assist matrix, an NHWC tensor of type float16. | |||
| * @kernel_matrix: Assist matrix, an NHWC tensor of type float16. | |||
| * @li input_grad: An NHWC tensor of type float16. | |||
| * @li mean_matrix: Assist matrix, an NHWC tensor of type float16. | |||
| * @li kernel_matrix: Assist matrix, an NHWC tensor of type float16. | |||
| * @par Attributes: | |||
| * @li orig_input_shape: A required Original input dimensions. | |||
| @@ -913,7 +913,7 @@ REG_OP(AvgPoolGrad) | |||
| * @li data_format: An optional string. Defaults to "NHWC" . \n | |||
| * @par Outputs: | |||
| * @out_grad: A mutable tensor with the same shape and type as "orig_input". | |||
| * @li out_grad: A mutable tensor with the same shape and type as "orig_input". | |||
| * | |||
| * @par Restrictions: | |||
| * Warning: THIS FUNCTION IS DEPRECATED. Please use AvgPoolGrad instead. | |||
+ 3
- 3
third_party/fwkacllib/inc/ops/reduce_ops.h
View File
| @@ -277,7 +277,7 @@ REG_OP(BN3DTrainingUpdate) | |||
| *y: A tensor of type float16 or float32 for the normalized "x" . \n | |||
| *@attention Constraints: | |||
| *For Ascend 310, the result accuracy fails to reach 1/1000 due to the | |||
| * For Ascend 310, the result accuracy fails to reach 1/1000 due to the | |||
| * square root instruction. | |||
| */ | |||
| REG_OP(BNInfer) | |||
| @@ -314,8 +314,8 @@ REG_OP(BNInfer) | |||
| *@li batch_variance: A tensor of type float32, for the variance of "x" . \n | |||
| *@attention Constraints: | |||
| *This operator is used in conjunction with BNTrainingReduce. | |||
| *For Ascend 310, the result accuracy fails to reach 1/1000 due to | |||
| *@li This operator is used in conjunction with BNTrainingReduce. | |||
| *@li For Ascend 310, the result accuracy fails to reach 1/1000 due to | |||
| * the square root instruction. | |||
| */ | |||
| REG_OP(BNTrainingUpdateV2) | |||
+ 2
- 1
third_party/fwkacllib/inc/runtime/config.h
View File
| @@ -83,7 +83,8 @@ typedef enum tagRtPlatformType { | |||
| PLATFORM_CLOUD_V2_910B2 = 11, | |||
| PLATFORM_CLOUD_V2_910B3 = 12, | |||
| PLATFORM_CLOUD_V2_910B4 = 13, | |||
| PLATFORM_END = 14, | |||
| PLATFORM_MDC_PG2 = 14, | |||
| PLATFORM_END = 15, | |||
| } rtPlatformType_t; | |||
| typedef enum tagRtCubeFracMKNFp16 { | |||
+ 4
- 4
third_party/fwkacllib/inc/runtime/dev.h
View File
| @@ -55,10 +55,10 @@ typedef enum tagRtFeatureType { | |||
| } rtFeatureType_t; | |||
| typedef enum tagRtDeviceFeatureType { | |||
| FEATURE_TYPE_SCHE, | |||
| FEATURE_TYPE_BLOCKING_OPERATOR, | |||
| FEATURE_TYPE_FFTS_MODE, | |||
| FEATURE_TYPE_END, | |||
| FEATURE_TYPE_SCHE, | |||
| FEATURE_TYPE_BLOCKING_OPERATOR, | |||
| FEATURE_TYPE_FFTS_MODE, | |||
| FEATURE_TYPE_END, | |||
| } rtDeviceFeatureType_t; | |||
| typedef enum tagMemcpyInfo { | |||
+ 34
- 1
third_party/fwkacllib/inc/runtime/mem.h
View File
| @@ -392,7 +392,23 @@ RTS_API rtError_t rtMemcpyHostTask(void * const dst, const uint64_t destMax, con | |||
| RTS_API rtError_t rtMemcpyAsync(void *dst, uint64_t destMax, const void *src, uint64_t cnt, rtMemcpyKind_t kind, | |||
| rtStream_t stm); | |||
| typedef struct rtMemcpyAddrInfo { | |||
| /** | |||
| * @ingroup dvrt_mem | |||
| * @brief asynchronized memcpy | |||
| * @param [in] dst destination address pointer | |||
| * @param [in] Max length of destination address memory | |||
| * @param [in] src source address pointer | |||
| * @param [in] count the number of byte to copy | |||
| * @param [in] kind memcpy type | |||
| * @param [in] stream asynchronized task stream | |||
| * @param [in] qosCfg asynchronized task qosCfg | |||
| * @return RT_ERROR_NONE for ok | |||
| * @return RT_ERROR_INVALID_VALUE for error input | |||
| */ | |||
| RTS_API rtError_t rtMemcpyAsyncWithCfg(void *dst, uint64_t destMax, const void *src, uint64_t count, | |||
| rtMemcpyKind_t kind, rtStream_t stream, uint32_t qosCfg); | |||
| typedef struct { | |||
| uint32_t resv0; | |||
| uint32_t resv1; | |||
| uint32_t resv2; | |||
| @@ -420,6 +436,23 @@ RTS_API rtError_t rtMemcpyAsyncPtr(void *memcpyAddrInfo, uint64_t destMax, uint6 | |||
| RTS_API rtError_t rtReduceAsync(void *dst, uint64_t destMax, const void *src, uint64_t cnt, rtRecudeKind_t kind, | |||
| rtDataType_t type, rtStream_t stm); | |||
| /** | |||
| * @ingroup dvrt_mem | |||
| * @brief asynchronized reduce memcpy | |||
| * @param [in] dst destination address pointer | |||
| * @param [in] Max length of destination address memory | |||
| * @param [in] src source address pointer | |||
| * @param [in] count the number of byte to copy | |||
| * @param [in] kind memcpy type | |||
| * @param [in] type data type | |||
| * @param [in] stm asynchronized task stream | |||
| * @param [in] qosCfg asynchronized task qosCfg | |||
| * @return RT_ERROR_NONE for ok | |||
| * @return RT_ERROR_INVALID_VALUE for error input | |||
| */ | |||
| RTS_API rtError_t rtReduceAsyncWithCfg(void *dst, uint64_t destMax, const void *src, uint64_t cnt, rtRecudeKind_t kind, | |||
| rtDataType_t type, rtStream_t stm, uint32_t qosCfg); | |||
| /** | |||
| * @ingroup dvrt_mem | |||
| * @brief asynchronized reduce memcpy | |||
+ 37
- 4
third_party/fwkacllib/inc/runtime/rt_mem_queue.h
View File
| @@ -372,6 +372,17 @@ RTS_API rtError_t rtMbufInit(rtMemBuffCfg_t *cfg); | |||
| */ | |||
| RTS_API rtError_t rtMbufAlloc(rtMbufPtr_t *memBuf, uint64_t size); | |||
| /** | |||
| * @ingroup rt_mem_queue | |||
| * @brief alloc buff | |||
| * @param [out] memBuf: buff addr alloced | |||
| * @param [in] size: The amount of memory space requested | |||
| * @param [in] flag: Huge page flag(bit0~31: mem type, bit32~bit35: devid, bit36~63: resv) | |||
| * @param [in] grpId: group id | |||
| * @return RT_ERROR_NONE for ok | |||
| */ | |||
| RTS_API rtError_t rtMbufAllocEx(rtMbufPtr_t *memBuf, uint64_t size, uint64_t flag, int32_t grpId); | |||
| /** | |||
| * @ingroup rt_mem_queue | |||
| * @brief free buff | |||
| @@ -417,6 +428,15 @@ RTS_API rtError_t rtMbufGetBuffSize(rtMbufPtr_t memBuf, uint64_t *totalSize); | |||
| */ | |||
| RTS_API rtError_t rtMbufGetPrivInfo(rtMbufPtr_t memBuf, void **priv, uint64_t *size); | |||
| /** | |||
| * @ingroup rt_mem_queue | |||
| * @brief copy buf ref | |||
| * @param [in] memBuf: src buff addr | |||
| * @param [out] newMemBuf: des buff addr | |||
| * @return RT_ERROR_NONE for ok | |||
| */ | |||
| RTS_API rtError_t rtMbufCopyBufRef(rtMbufPtr_t memBuf, rtMbufPtr_t *newMemBuf); | |||
| // mem group | |||
| typedef struct { | |||
| uint64_t maxMemSize; // max buf size in grp, in KB. = 0 means no limit | |||
| @@ -430,30 +450,43 @@ typedef struct { | |||
| uint32_t rsv : 28; | |||
| } rtMemGrpShareAttr_t; | |||
| #define RT_MEM_GRP_QUERY_GROUPS_OF_PROCESS 1 // query process all grp | |||
| #define RT_MEM_GRP_QUERY_GROUPS_OF_PROCESS 1 // query process all grp | |||
| #define RT_MEM_GRP_QUERY_GROUP_ID 2 // query group id from name | |||
| #define RT_MEM_GRP_NAME_LEN 32 // it must be same as driver define BUFF_GRP_NAME_LEN | |||
| typedef struct { | |||
| int32_t pid; | |||
| } rtMemGrpQueryByProc_t; // cmd: GRP_QUERY_GROUPS_OF_PROCESS | |||
| typedef struct { | |||
| char grpName[RT_MEM_GRP_NAME_LEN]; | |||
| } rtMemGrpQueryGroupId_t; // cmd: RT_MEM_GRP_QUERY_GROUP_ID | |||
| typedef struct { | |||
| int32_t cmd; | |||
| union { | |||
| rtMemGrpQueryByProc_t grpQueryByProc; // cmd: GRP_QUERY_GROUPS_OF_PROCESS | |||
| rtMemGrpQueryGroupId_t grpQueryGroupId; // cmd: RT_MEM_GRP_QUERY_GROUP_ID | |||
| }; | |||
| } rtMemGrpQueryInput_t; | |||
| #define RT_MEM_GRP_NAME_LEN 32 // it must be same as driver define BUFF_GRP_NAME_LEN | |||
| typedef struct { | |||
| char_t groupName[RT_MEM_GRP_NAME_LEN]; // group name | |||
| rtMemGrpShareAttr_t attr; // process in group attribute | |||
| } rtMemGrpOfProc_t; // cmd: GRP_QUERY_GROUPS_OF_PROCESS | |||
| typedef struct { | |||
| rtMemGrpOfProc_t *groupsOfProc; // cmd: GRP_QUERY_GROUPS_OF_PROCESS | |||
| int32_t groupId; // group id | |||
| } rtMemGrpQueryGroupIdInfo_t; // cmd: RT_MEM_GRP_QUERY_GROUP_ID | |||
| typedef struct { | |||
| size_t maxNum; // max number of result | |||
| size_t resultNum; // if the number of results exceeds 'maxNum', only 'maxNum' results are filled in buffer | |||
| union { | |||
| rtMemGrpOfProc_t *groupsOfProc; // cmd: GRP_QUERY_GROUPS_OF_PROCESS | |||
| rtMemGrpQueryGroupIdInfo_t *groupIdInfo; // cmd: RT_MEM_GRP_QUERY_GROUP_ID | |||
| }; | |||
| } rtMemGrpQueryOutput_t; | |||
| /** | |||
+ 60
- 2
third_party/fwkacllib/inc/runtime/rt_stars.h
View File
| @@ -99,8 +99,66 @@ RTS_API rtError_t rtCmoTaskLaunch(rtCmoTaskInfo_t *taskInfo, rtStream_t stm, uin | |||
| * @return RT_ERROR_NONE for ok, others failed | |||
| */ | |||
| RTS_API rtError_t rtBarrierTaskLaunch(rtBarrierTaskInfo_t *taskInfo, rtStream_t stm, uint32_t flag); | |||
| #if defined(__cplusplus) | |||
| /** | |||
| * @ingroup rt_stars | |||
| * @brief dvpp group handle. | |||
| */ | |||
| typedef void *rtDvppGrp_t; | |||
| typedef struct tagDvppGrpRptInfo { | |||
| uint32_t deviceId; | |||
| uint32_t streamId; | |||
| uint32_t taskId; | |||
| uint8_t sqeType; | |||
| uint8_t cqeErrorCode; | |||
| uint8_t reserve[2]; | |||
| uint32_t accErrorCode; | |||
| } rtDvppGrpRptInfo_t; | |||
| typedef void (*rtDvppGrpCallback)(rtDvppGrpRptInfo_t *rptInfo); | |||
| /** | |||
| * @ingroup rt_stars | |||
| * @brief create dvpp group. | |||
| * @param [in] flags group flag, reserved parameter | |||
| * @param [out] grp group handle | |||
| * @return RT_ERROR_NONE for ok, others failed | |||
| */ | |||
| RTS_API rtError_t rtDvppGroupCreate(rtDvppGrp_t *grp, uint32_t flags); | |||
| /** | |||
| * @ingroup rt_stars | |||
| * @brief destroy dvpp group. | |||
| * @param [in] grp group handle | |||
| * @return RT_ERROR_NONE for ok, others failed | |||
| */ | |||
| RTS_API rtError_t rtDvppGroupDestory(rtDvppGrp_t grp); | |||
| /** | |||
| * @ingroup rt_stars | |||
| * @brief create stream with grp handle | |||
| * @param [in|out] stm created stream | |||
| * @param [in] priority stream priority | |||
| * @param [in] flags stream op flags | |||
| * @param [in] grp grp handle | |||
| * @return RT_ERROR_NONE for ok | |||
| * @return RT_ERROR_INVALID_VALUE for error input | |||
| * @return RT_ERROR_NONE for ok, others failed | |||
| */ | |||
| RTS_API rtError_t rtStreamCreateByGrp(rtStream_t *stm, int32_t priority, uint32_t flags, rtDvppGrp_t grp); | |||
| /** | |||
| * @ingroup rt_stars | |||
| * @brief wait report by grp | |||
| * @param [in] grp group handle | |||
| * @param [in] callBackFunc callback | |||
| * @param [in] timeout wait timeout config, ms, -1: wait forever | |||
| * @return RT_ERROR_NONE for ok, others failed | |||
| */ | |||
| RTS_API rtError_t rtDvppWaitGroupReport(rtDvppGrp_t grp, rtDvppGrpCallback callBackFunc, int32_t timeout); | |||
| #if defined(__cplusplus) | |||
| } | |||
| #endif | |||
| #endif // CCE_RUNTIME_RT_STARS_H | |||
| #endif // CCE_RUNTIME_RT_STARS_H | |||
+ 11
- 1
third_party/fwkacllib/inc/runtime/stream.h
View File
| @@ -101,6 +101,16 @@ RTS_API rtError_t rtStreamWaitEventWithTimeout(rtStream_t stm, rtEvent_t evt, ui | |||
| */ | |||
| RTS_API rtError_t rtStreamSynchronize(rtStream_t stm); | |||
| /** | |||
| * @ingroup dvrt_stream | |||
| * @brief wait stream to be complete and set timeout | |||
| * @param [in] stm stream to wait | |||
| * @param [in] timeout timeout value,the unit is milliseconds | |||
| * @return RT_ERROR_NONE for ok | |||
| * @return RT_ERROR_INVALID_VALUE for error input | |||
| */ | |||
| RTS_API rtError_t rtStreamSynchronizeWithTimeout(rtStream_t stm, int32_t timeout); | |||
| /** | |||
| * @ingroup dvrt_stream | |||
| * @brief queries an asynchronous stream for completion status | |||
| @@ -202,7 +212,7 @@ RTS_API rtError_t rtStreamSwitchN(void *ptr, uint32_t size, void *valuePtr, rtSt | |||
| * @return RT_ERROR_INVALID_VALUE for error input | |||
| */ | |||
| RTS_API rtError_t rtDebugRegisterForStream(rtStream_t stm, uint32_t flag, const void *addr, | |||
| uint32_t *streamId, uint32_t *taskId); | |||
| uint32_t *streamId, uint32_t *taskId); | |||
| /* | |||
| * @ingroup rt_model | |||
+ 5
- 1
third_party/fwkacllib/inc/toolchain/prof_acl_api.h
View File
| @@ -18,6 +18,8 @@ | |||
| #define PROF_TRAINING_TRACE 0x00000040ULL | |||
| #define PROF_MSPROFTX 0x00000080ULL | |||
| #define PROF_RUNTIME_API 0x00000100ULL | |||
| #define PROF_TASK_FRAMEWORK 0x00000200ULL | |||
| #define PROF_TASK_TSFW 0x00000400ULL | |||
| // system profilinig switch | |||
| #define PROF_CPU 0x00010000ULL | |||
| @@ -52,6 +54,8 @@ constexpr uint64_t PROF_AICPU_MODEL = 0x4000000000000000ULL; | |||
| #define PROF_TRAINING_TRACE_MASK 0x00000040ULL | |||
| #define PROF_MSPROFTX_MASK 0x00000080ULL | |||
| #define PROF_RUNTIME_API_MASK 0x00000100ULL | |||
| #define PROF_TASK_FRAMEWORK_MASK 0x00000200ULL | |||
| #define PROF_TASK_TSFW_MASK 0x00000400ULL | |||
| // system profilinig mask | |||
| #define PROF_CPU_MASK 0x00010000ULL | |||
| @@ -102,7 +106,7 @@ extern "C" { | |||
| MSVP_PROF_API uint64_t ProfGetOpExecutionTime(const void *data, uint32_t len, uint32_t index); | |||
| typedef int32_t Status; | |||
| using Status = int32_t; | |||
| typedef struct aclprofSubscribeConfig aclprofSubscribeConfig1; | |||
| /// | |||
| /// @ingroup AscendCL | |||
+ 6
- 0
third_party/fwkacllib/inc/toolchain/prof_common.h
View File
| @@ -159,8 +159,14 @@ enum MsprofGeTaskType { | |||
| MSPROF_GE_TASK_TYPE_AI_CPU, | |||
| MSPROF_GE_TASK_TYPE_AIV, | |||
| MSPROF_GE_TASK_TYPE_WRITE_BACK, | |||
| MSPROF_GE_TASK_TYPE_MIX_AIC, | |||
| MSPROF_GE_TASK_TYPE_MIX_AIV, | |||
| MSPROF_GE_TASK_TYPE_FFTS_PLUS, | |||
| MSPROF_GE_TASK_TYPE_DSA, | |||
| MSPROF_GE_TASK_TYPE_DVPP, | |||
| MSPROF_GE_TASK_TYPE_INVALID | |||
| }; | |||
| enum MsprofGeShapeType { | |||
| MSPROF_GE_SHAPE_TYPE_STATIC = 0, | |||
| MSPROF_GE_SHAPE_TYPE_DYNAMIC, | |||